This document outlines detailed design of the reference imprementations. This allows developers and users to understand what is currently available with the Perception Component, how to utilize, expand, or add to its features.
Autoware is the world\u2019s leading open-source software project for autonomous driving. Autoware is built on Robot Operating System (ROS) and enables commercial deployment of autonomous driving in a broad range of vehicles and applications.
Please see here for more details.
"},{"location":"#related-documentations","title":"Related Documentations","text":"This Autoware Documentation is for Autoware's general information.
For detailed documents of Autoware Universe components, see Autoware Universe Documentation.
"},{"location":"#getting-started","title":"Getting started","text":"Thank you for your interest in contributing! Autoware is supported by people like you, and all types and sizes of contribution are welcome.
As a contributor, here are the guidelines that we would like you to follow for Autoware and its associated repositories.
Like Autoware itself, these guidelines are being actively developed and suggestions for improvement are always welcome! Guideline changes can be proposed by creating a discussion in the Ideas category.
"},{"location":"contributing/#code-of-conduct","title":"Code of Conduct","text":"To ensure the Autoware community stays open and inclusive, please follow the Code of Conduct.
If you believe that someone in the community has violated the Code of Conduct, please make a report by emailing conduct@autoware.org.
"},{"location":"contributing/#what-should-i-know-before-i-get-started","title":"What should I know before I get started?","text":""},{"location":"contributing/#autoware-concepts","title":"Autoware concepts","text":"To gain a high-level understanding of Autoware's architecture and design, the following pages provide a brief overview:
For experienced developers, the Autoware interfaces and individual component pages should also be reviewed to understand the inputs and outputs for each component or module at a more detailed level.
"},{"location":"contributing/#contributing-to-open-source-projects","title":"Contributing to open source projects","text":"If you are new to open source projects, we recommend reading GitHub's How to Contribute to Open Source guide for an overview of why people contribute to open source projects, what it means to contribute and much more besides.
"},{"location":"contributing/#how-can-i-get-help","title":"How can I get help?","text":"Do not open issues for general support questions as we want to keep GitHub issues for confirmed bug reports. Instead, open a discussion in the Q&A category. For more details on the support mechanisms for Autoware, refer to the Support guidelines.
Note
Issues created for questions or unconfirmed bugs will be moved to GitHub discussions by the maintainers.
"},{"location":"contributing/#how-can-i-contribute","title":"How can I contribute?","text":""},{"location":"contributing/#discussions","title":"Discussions","text":"You can contribute to Autoware by facilitating and participating in discussions, such as:
The various working groups within the Autoware Foundation are responsible for accomplishing goals set by the Technical Steering Committee. These working groups are open to everyone, and joining a particular working group will allow you to gain an understanding of current projects, see how those projects are managed within each group and to contribute to issues that will help progress a particular project.
To see the schedule for upcoming working group meetings, refer to the Autoware Foundation events calendar.
"},{"location":"contributing/#bug-reports","title":"Bug reports","text":"Before you report a bug, please search the issue tracker for the appropriate repository. It is possible that someone has already reported the same issue and that workarounds exist. If you can't determine the appropriate repository, ask the maintainers for help by creating a new discussion in the Q&A category.
When reporting a bug, you should provide a minimal set of instructions to reproduce the issue. Doing so allows us to quickly confirm and focus on the right problem.
If you want to fix the bug by yourself that will be appreciated, but you should discuss possible approaches with the maintainers in the issue before submitting a pull request.
Creating an issue is straightforward, but if you happen to experience any problems then create a Q&A discussion to ask for help.
"},{"location":"contributing/#pull-requests","title":"Pull requests","text":"You can submit pull requests for small changes such as:
If your pull request is a large change, the following process should be followed:
Create a GitHub Discussion to propose the change. Doing so allows you to get feedback from other members and the Autoware maintainers and to ensure that the proposed change is in line with Autoware's design philosophy and current development plans. If you're not sure where to have that conversation, then create a new Q&A discussion.
Create an issue following consensus in the discussions
Create a pull request to implement the changes that references the Issue created in step 2
Create documentation for the new addition (if relevant)
Examples of large changes include:
For more information on how to submit a good pull request, have a read of the pull request guidelines and don't forget to review the required license notations!
"},{"location":"contributing/license/","title":"License","text":""},{"location":"contributing/license/#license","title":"License","text":"Autoware is licensed under Apache License 2.0. Thus all contributions will be licensed as such as per clause 5 of the Apache License 2.0:
5. Submission of Contributions. Unless You explicitly state otherwise,\n any Contribution intentionally submitted for inclusion in the Work\n by You to the Licensor shall be under the terms and conditions of\n this License, without any additional terms or conditions.\n Notwithstanding the above, nothing herein shall supersede or modify\n the terms of any separate license agreement you may have executed\n with Licensor regarding such Contributions.\nHere is an example copyright header to add to the top of a new file:
Copyright [first year of contribution] The Autoware Contributors\nSPDX-License-Identifier: Apache-2.0\nWe don't write copyright notations of each contributor here. Instead, we place them in the NOTICE file like the following.
This product includes code developed by [company name].\nCopyright [first year of contribution] [company name]\nLet us know if your legal department has a special request for the copyright notation.
Currently, the old formats explained here are also acceptable. Those old formats can be replaced by this new format if the original authors agree. Note that we won't write their copyrights to the NOTICE file unless they agree with the new format.
References:
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"},{"location":"contributing/coding-guidelines/#common-guidelines","title":"Common guidelines","text":"Refer to the following links for now:
Also, keep in mind the following concepts.
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Refer to the following links for now:
To reduce duplications in CMakeLists.txt, there is the autoware_package() macro. See the README and use it in your package.
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"},{"location":"contributing/coding-guidelines/languages/cpp/#references","title":"References","text":"Follow the guidelines below if a rule is not defined on this page.
Also, it is encouraged to apply Clang-Tidy to each file. For the usage, see Applying Clang-Tidy to ROS packages.
Note that not all rules are covered by Clang-Tidy.
"},{"location":"contributing/coding-guidelines/languages/cpp/#style-rules","title":"Style rules","text":""},{"location":"contributing/coding-guidelines/languages/cpp/#include-header-files-in-the-defined-order-required-partially-automated","title":"Include header files in the defined order (required, partially automated)","text":""},{"location":"contributing/coding-guidelines/languages/cpp/#rationale","title":"Rationale","text":"Include the headers in the following order:
// Compliant\n#include \"my_header.hpp\"\n\n#include \"my_package/foo.hpp\"\n\n#include <package1/foo.hpp>\n#include <package2/bar.hpp>\n\n#include <std_msgs/msg/header.hpp>\n\n#include <iostream>\n#include <vector>\nIf you use \"\" and <> properly, ClangFormat in pre-commit sorts headers automatically.
Do not define macros between #include lines because it prevents automatic sorting.
// Non-compliant\n#include <package1/foo.hpp>\n#include <package2/bar.hpp>\n\n#define EIGEN_MPL2_ONLY\n#include \"my_header.hpp\"\n#include \"my_package/foo.hpp\"\n\n#include <Eigen/Core>\n\n#include <std_msgs/msg/header.hpp>\n\n#include <iostream>\n#include <vector>\nInstead, define macros before #include lines.
// Compliant\n#define EIGEN_MPL2_ONLY\n\n#include \"my_header.hpp\"\n\n#include \"my_package/foo.hpp\"\n\n#include <Eigen/Core>\n#include <package1/foo.hpp>\n#include <package2/bar.hpp>\n\n#include <std_msgs/msg/header.hpp>\n\n#include <iostream>\n#include <vector>\nIf there are any reasons for defining macros at a specific position, write a comment before the macro.
// Compliant\n#include \"my_header.hpp\"\n\n#include \"my_package/foo.hpp\"\n\n#include <package1/foo.hpp>\n#include <package2/bar.hpp>\n\n#include <std_msgs/msg/header.hpp>\n\n#include <iostream>\n#include <vector>\n\n// For the foo bar reason, the FOO_MACRO must be defined here.\n#define FOO_MACRO\n#include <foo/bar.hpp>\nvoid function_name()\n{\n}\nrosidl file can use other naming conventions.enum class Color\n{\nRed, Green, Blue\n}\nstd::numbers.rosidl file can use other naming conventions.constexpr double gravity = 9.80665;\nclass RosApi;\nRosApi ros_api;\nWarning
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Refer to the following links for now:
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Refer to the following links for now:
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Refer to the following links for now:
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Refer to the following links for now:
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Refer to the following links for now:
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Refer to the following links for now:
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"},{"location":"contributing/coding-guidelines/ros-nodes/console-logging/","title":"Console logging","text":""},{"location":"contributing/coding-guidelines/ros-nodes/console-logging/#console-logging","title":"Console logging","text":"ROS 2 logging is a powerful tool for understanding and debugging ROS nodes.
This page focuses on how to design console logging in Autoware and shows several practical examples. To comprehensively understand how ROS 2 logging works, refer to the logging documentation.
"},{"location":"contributing/coding-guidelines/ros-nodes/console-logging/#logging-use-cases-in-autoware","title":"Logging use cases in Autoware","text":"To efficiently support these use cases, clean and highly visible logs are required. For that, several rules are defined below.
"},{"location":"contributing/coding-guidelines/ros-nodes/console-logging/#rules","title":"Rules","text":""},{"location":"contributing/coding-guidelines/ros-nodes/console-logging/#choose-appropriate-severity-levels-required-non-automated","title":"Choose appropriate severity levels (required, non-automated)","text":""},{"location":"contributing/coding-guidelines/ros-nodes/console-logging/#rationale","title":"Rationale","text":"It's confusing if severity levels are inappropriate as follows:
FATAL.INFO.Use the following criteria as a reference:
Some third-party nodes such as drivers may not follow the Autoware's guidelines. If the logs are noisy, unnecessary logs should be filtered out.
"},{"location":"contributing/coding-guidelines/ros-nodes/console-logging/#example_1","title":"Example","text":"Use the --log-level {level} option to change the minimum level of logs to be displayed:
<launch>\n<!-- This outputs only FATAL level logs. -->\n<node pkg=\"demo_nodes_cpp\" exec=\"talker\" ros_args=\"--log-level fatal\" />\n</launch>\nIf you want to disable only specific output targets, use the --disable-stdout-logs, --disable-rosout-logs, and/or --disable-external-lib-logs options:
<launch>\n<!-- This outputs to rosout and disk. -->\n<node pkg=\"demo_nodes_cpp\" exec=\"talker\" ros_args=\"--disable-stdout-logs\" />\n</launch>\n<launch>\n<!-- This outputs to stdout. -->\n<node pkg=\"demo_nodes_cpp\" exec=\"talker\" ros_args=\"--disable-rosout-logs --disable-external-lib-logs\" />\n</launch>\nIf tons of logs are shown on the console, people miss important message.
"},{"location":"contributing/coding-guidelines/ros-nodes/console-logging/#example_2","title":"Example","text":"While waiting for some messages, throttled logs are usually enough. In such cases, wait about 5 seconds as a reference value.
// Compliant\nvoid FooNode::on_timer() {\nif (!current_pose_) {\nRCLCPP_ERROR_THROTTLE(get_logger(), *get_clock(), 5000, \"Waiting for current_pose_.\");\nreturn;\n}\n}\n\n// Non-compliant\nvoid FooNode::on_timer() {\nif (!current_pose_) {\nRCLCPP_ERROR(get_logger(), \"Waiting for current_pose_.\");\nreturn;\n}\n}\nThe following cases are acceptable even if it's not throttled.
Core library classes, which contain reusable algorithms, may also be used for non-ROS platforms. When porting libraries to other platforms, fewer dependencies are preferred.
"},{"location":"contributing/coding-guidelines/ros-nodes/console-logging/#example_3","title":"Example","text":"// Compliant\n#include <rclcpp/logging.hpp>\n\nclass FooCore {\npublic:\nexplicit FooCore(const rclcpp::Logger & logger) : logger_(logger) {}\n\nvoid process() {\nRCLCPP_INFO(logger_, \"message\");\n}\n\nprivate:\nrclcpp::Logger logger_;\n};\n\n// Compliant\n// Note that logs aren't published to `/rosout` if the logger name is different from the node name.\n#include <rclcpp/logging.hpp>\n\nclass FooCore {\nvoid process() {\nRCLCPP_INFO(rclcpp::get_logger(\"foo_core_logger\"), \"message\");\n}\n};\n\n\n// Non-compliant\n#include <rclcpp/node.hpp>\n\nclass FooCore {\npublic:\nexplicit FooCore(const rclcpp::NodeOptions & node_options) : node_(\"foo_core_node\", node_options) {}\n\nvoid process() {\nRCLCPP_INFO(node_.get_logger(), \"message\");\n}\n\nprivate:\nrclcpp::Node node_;\n};\nTo filter logs, using rqt_console is useful:
ros2 run rqt_console rqt_console\nFor more details, refer to ROS 2 Documentation.
"},{"location":"contributing/coding-guidelines/ros-nodes/console-logging/#useful-marco-expressions","title":"Useful marco expressions","text":"To debug program, sometimes you need to see which functions and lines of code are executed. In that case, you can use __FILE__, __LINE__ and __FUNCTION__ macro:
void FooNode::on_timer() {\nRCLCPP_DEBUG(get_logger(), \"file: %s, line: %s, function: %s\" __FILE__, __LINE__, __FUNCTION__);\n}\nThe example output is as follows:
[DEBUG] [1671720414.395456931] [foo]: file: /path/to/file.cpp, line: 100, function: on_timer
"},{"location":"contributing/coding-guidelines/ros-nodes/coordinate-system/","title":"Coordinate system","text":""},{"location":"contributing/coding-guidelines/ros-nodes/coordinate-system/#coordinate-system","title":"Coordinate system","text":""},{"location":"contributing/coding-guidelines/ros-nodes/coordinate-system/#overview","title":"Overview","text":"The commonly used coordinate systems include the world coordinate system, the vehicle coordinate system, and the sensor coordinate system.
In Autoware, coordinate systems are typically used to represent the position and movement of vehicles and obstacles in space. Coordinate systems are commonly used for path planning, perception and control, can help the vehicle decide how to avoid obstacles and to plan a safe and efficient path of travel.
Transformation of sensor data
In Autoware, each sensor has a unique coordinate system and their data is expressed in terms of the coordinates. In order to correlate the independent data between different sensors, we need to find the position relationship between each sensor and the vehicle body. Once the installation position of the sensor on the vehicle body is determined, it will remain fixed during running, so the offline calibration method can be used to determine the precise position of each sensor relative to the vehicle body.
ROS TF2
The TF2 system maintains a tree of coordinate transformations to represent the relationships between different coordinate systems. Each coordinate system is given a unique name and they are connected by coordinate transformations. How to use TF2, refer to the TF2 tutorial.
In Autoware, a common coordinate system structure is shown below:
graph TD\n /earth --> /map\n /map --> /base_link\n /base_link --> /imu\n /base_link --> /lidar\n /base_link --> /gnss\n /base_link --> /radar\n /base_link --> /camera_link\n /camera_link --> /camera_optical_linkearth coordinate system describe the position of any point on the earth in terms of geodetic longitude, latitude, and altitude. In Autoware, the earth frame is only used in the GnssInsPositionStamped message.map coordinate system is used to represent the location of points on a local map. Geographical coordinate system are mapped into plane rectangular coordinate system using UTM or MGRS. The map frame`s axes point to the East, North, Up directions as explained in Coordinate Axes Conventions.camera_link is ROS standard camera coordinate system .camera_optical_link is image standard camera coordinate system.base_link frame by using the other sensors","text":"Generally we don't have the localization sensors physically at the base_link frame. So various sensors localize with respect to their own frames, let's call it sensor frame.
We introduce a new frame naming convention: x_by_y:
x: estimated frame name\ny: localization method/source\nWe cannot directly get the sensor frame. Because we would need the EKF module to estimate the base_link frame first.
Without the EKF module the best we can do is to estimate Map[map] --> sensor_by_sensor --> base_link_by_sensor using this sensor.
For the integrated GNSS/INS we use the following frames:
flowchart LR\n earth --> Map[map] --> gnss_ins_by_gnss_ins --> base_link_by_gnss_insThe gnss_ins_by_gnss_ins frame is obtained by the coordinates from GNSS/INS sensor. The coordinates are converted to map frame using the gnss_poser node.
Finally gnss_ins_by_gnss_ins frame represents the position of the gnss_ins estimated by the gnss_ins sensor in the map.
Then by using the static transformation between gnss_ins and the base_link frame, we can obtain the base_link_by_gnss_ins frame. Which represents the base_link estimated by the gnss_ins sensor.
References:
We are using East, North, Up (ENU) coordinate axes convention by default throughout the stack.
X+: East\nY+: North\nZ+: Up\nThe position, orientation, velocity, acceleration are all defined in the same axis convention.
Position by the GNSS/INS sensor is expected to be in earth frame.
Orientation, velocity, acceleration by the GNSS/INS sensor are expected to be in the sensor frame. Axes parallel to the map frame.
If roll, pitch, yaw is provided, they correspond to rotation around X, Y, Z axes respectively.
Rotation around:\nX+: roll\nY+: pitch\nZ+: yaw\nReferences:
Calibration of sensor
The conversion relationship between every sensor coordinate system and base_link can be obtained through sensor calibration technology. How to calibrating your sensors refer to this link calibrating your sensors.
Localization
The relationship between the base_link coordinate system and the map coordinate system is determined by the position and orientation of the vehicle, and can be obtained from the vehicle localization result.
Geo-referencing of map data
The geo-referencing information can get the transformation relationship of earth coordinate system to local map coordinate system.
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"},{"location":"contributing/coding-guidelines/ros-nodes/directory-structure/#c-package","title":"C++ package","text":"<package_name>\n\u251c\u2500 config\n\u2502 \u251c\u2500 foo_ros.param.yaml\n\u2502 \u2514\u2500 foo_non_ros.yaml\n\u251c\u2500 include\n\u2502 \u2514\u2500 <package_name>\n\u2502 \u2514\u2500 foo_public.hpp\n\u251c\u2500 launch\n\u2502 \u251c\u2500 foo.launch.xml\n\u2502 \u2514\u2500 foo.launch.py\n\u251c\u2500 src\n\u2502 \u251c\u2500 foo_node.cpp\n\u2502 \u251c\u2500 foo_node.hpp\n\u2502 \u2514\u2500 foo_private.hpp\n\u251c\u2500 test\n\u2502 \u2514\u2500 test_foo.cpp\n\u251c\u2500 package.xml\n\u2514\u2500 CMakeLists.txt\nPlace configuration files such as node parameters.
For ROS parameters, use the extension .param.yaml. For non-ROS parameters, use the extension .yaml.
Rationale: Since ROS parameters files are type-sensitive, they should not be the target of some code formatters and linters. In order to distinguish the file type, we use different file extensions.
"},{"location":"contributing/coding-guidelines/ros-nodes/directory-structure/#include-directory","title":"include directory","text":"Place header files exposed to other packages. Do not place files directly under the include directory, but place files under the directory with the package name. This directory is used for mostly library headers. Note that many headers do not need to be placed here. It is enough to place the headers under the src directory.
Reference: https://docs.ros.org/en/rolling/How-To-Guides/Ament-CMake-Documentation.html#adding-files-and-headers
"},{"location":"contributing/coding-guidelines/ros-nodes/directory-structure/#launch-directory","title":"launch directory","text":"Place launch files (.launch.xml and .launch.py).
Place source files and private header files.
"},{"location":"contributing/coding-guidelines/ros-nodes/directory-structure/#test-directory","title":"test directory","text":"Place source files for testing.
"},{"location":"contributing/coding-guidelines/ros-nodes/directory-structure/#python-package","title":"Python package","text":"T.B.D.
"},{"location":"contributing/coding-guidelines/ros-nodes/launch-files/","title":"Launch files","text":""},{"location":"contributing/coding-guidelines/ros-nodes/launch-files/#launch-files","title":"Launch files","text":""},{"location":"contributing/coding-guidelines/ros-nodes/launch-files/#overview","title":"Overview","text":"Autoware use ROS2 launch system to startup the software. Please see the official documentation to get a basic understanding about ROS 2 Launch system if you are not familiar with it.
"},{"location":"contributing/coding-guidelines/ros-nodes/launch-files/#guideline","title":"Guideline","text":""},{"location":"contributing/coding-guidelines/ros-nodes/launch-files/#the-organization-of-launch-files-in-autoware","title":"The organization of launch files in Autoware","text":"Autoware mainly has two repositories related to launch file organization: the autoware.universe and the autoware_launch.
"},{"location":"contributing/coding-guidelines/ros-nodes/launch-files/#autowareuniverse","title":"autoware.universe","text":"the autoware.universe contains the code of the main Autoware modules, and its launch directory is responsible for launching the nodes of each module. Autoware software stack is organized based on the architecture, so you may find that we try to match the launch structure similar to the architecture (splitting of files, namespace). For example, the tier4_map_launch subdirectory corresponds to the map module, so do the other tier4_*_launch subdirectories.
The autoware_launch is a repository referring to autoware.universe. The mainly purpose of introducing this repository is to provide the general entrance to start the Autoware software stacks, i.e, calling the launch file of each module.
The autoware.launch.xml is the basic launch file for road driving scenarios.
As can be seen from the content, the entire launch file is divided into several different modules, including Vehicle, System, Map, Sensing, Localization, Perception, Planning, Control, etc. By setting the launch_* argument equals to true or false , we can determine which modules to be loaded.
logging_simulator.launch.xml is often used together with the recorded ROS bag to debug if the target module (e.g, Sensing, Localization or Perception) functions normally.planning_simulator.launch.xml is based on the Planning Simulator tool, mainly used for testing/validation of Planning module by simulating traffic rules, interactions with dynamic objects and control commands to the ego vehicle.e2e_simulator.launch.xml is the launcher for digital twin simulation environment.graph LR\nA11[logging_simulator.launch.xml]-.->A10[autoware.launch.xml]\nA12[planning_simulator.launch.xml]-.->A10[autoware.launch.xml]\nA13[e2e_simulator.launch.xml]-.->A10[autoware.launch.xml]\n\nA10-->A21[tier4_map_component.launch.xml]\nA10-->A22[xxx.launch.py]\nA10-->A23[tier4_localization_component.launch.xml]\nA10-->A24[xxx.launch.xml]\nA10-->A25[tier4_sensing_component.launch.xml]\n\nA23-->A30[localization.launch.xml]\nA30-->A31[pose_estimator.launch.xml]\nA30-->A32[util.launch.xml]\nA30-->A33[pose_twist_fusion_filter.launch.xml]\nA30-->A34[xxx.launch.xml]\nA30-->A35[twist_estimator.launch.xml]\n\nA33-->A41[stop_filter.launch.xml]\nA33-->A42[ekf_localizer.launch.xml]\nA33-->A43[twist2accel.launch.xml]If a newly created package has executable node, we expect sample launch file and configuration within the package, just like the recommended structure shown in previous directory structure page.
In order to automatically load the newly added package when starting Autoware, you need to make some necessary changes to the corresponding launch file. For example, if using ICP instead of NDT as the pointcloud registration algorithm, you can modify the autoware.universe/launch/tier4_localization_launch/launch/pose_estimator/pose_estimator.launch.xml file to load the newly added ICP package.
Another purpose of introducing the autoware_launch repository is to facilitate the parameter management of Autoware. Thinking about this situation: if we want to integrate Autoware to a specific vehicle and modify parameters, we have to fork autoware.universe which also has a lot of code other than parameters and is frequently updated by developers. By intergrating these parameters in autoware_launch, we can customize the Autoware parameters just by forking autoware_launch repository. Taking the localization module as an examples:
autoware_launch/autoware_launch/config/localization.autoware_launch/autoware_launch/launch/components/tier4_localization_component.launch.xml file.autoware.universe/launch/tier4_localization_launch/launch, the launch files loads the \u201claunch parameters\u201d if the argument is given in the parameter configuration file. You can still use the default parameters in each packages to launch tier4_localization_launch within autoware.universe.All messages should follow ROS message description specification.
The accepted formats are:
.msg.srv.actionUnder Construction
Use Array as a suffix when creating a plural type of a message. This suffix is commonly used in common_interfaces.
All the fields by default have the following units depending on their types:
type default unit distance meter (m) angle radians (rad) time second (s) speed m/s velocity m/s acceleration m/s\u00b2 angular vel. rad/s angular accel. rad/s\u00b2If a field in a message has any of these default units, don't add any suffix or prefix denoting the type.
"},{"location":"contributing/coding-guidelines/ros-nodes/message-guidelines/#non-default-units","title":"Non-default units","text":"For non-default units, use following suffixes:
type non-default unit suffix distance nanometer_nm distance micrometer _um distance millimeter _mm distance kilometer _km angle degree (deg) _deg time nanosecond _ns time microsecond _us time millisecond _ms time minute _min time hour (h) _hour velocity km/h _kmph If a unit that you'd like to use doesn't exist here, create an issue/PR to add it to this list.
"},{"location":"contributing/coding-guidelines/ros-nodes/message-guidelines/#message-field-types","title":"Message field types","text":"For list of types supported by the ROS interfaces see here.
Also copied here for convenience:
Message Field Type C++ equivalentbool bool byte uint8_t char char float32 float float64 double int8 int8_t uint8 uint8_t int16 int16_t uint16 uint16_t int32 int32_t uint32 uint32_t int64 int64_t uint64 uint64_t string std::string wstring std::u16string"},{"location":"contributing/coding-guidelines/ros-nodes/message-guidelines/#arrays","title":"Arrays","text":"For arrays, use unbounded dynamic array type.
Example:
int32[] unbounded_integer_array\nROS 2 interfaces don't support enumerations directly.
It is possible to define integers constants and assign them to a non-constant integer parameter.
Constants are written in CONSTANT_CASE.
Assign a different value to each element of a constant.
Example from shape_msgs/msg/SolidPrimitive.msg
uint8 BOX=1\nuint8 SPHERE=2\nuint8 CYLINDER=3\nuint8 CONE=4\nuint8 PRISM=5\n\n# The type of the shape\nuint8 type\nOn top of the message, briefly explain what the message contains and/or what it is used for. For an example, see sensor_msgs/msg/Imu.msg.
If necessary, add line comments before the fields that explain the context and/or meaning.
For simple fields like x, y, z, w you might not need to add comments.
Even though it is not strictly checked, try not to pass 100 characters in a line.
Example:
# Number of times the vehicle performed an emergency brake\nuint32 count_emergency_brake\n\n# Seconds passed since the last emergency brake\nuint64 duration\nfloat32 path_length_mfloat32 path_lengthfloat32 kmph_velocity_vehiclefloat32 velocity_vehicle_kmphfloat32 velocity_vehicle_km_hfloat32 velocity_vehicle_kmphThe ROS packages in Autoware have ROS parameters. You need to customize the parameters depending on your applications. It is recommended not to set default values when declaring ROS parameters to avoid unintended behaviors due to accidental use of default values. Instead, set parameters from configuration files named *.param.yaml.
For understanding ROS 2 parameters, also check out the official documentation Understanding parameters.
"},{"location":"contributing/coding-guidelines/ros-nodes/parameters/#parameter-files","title":"Parameter files","text":"Autoware has the following two types of parameter files for ROS packages:
behavior_path_plannerFOO_package, the parameter is expected to be stored in FOO_package/config.<launch>\n<arg name=\"foo_node_param_path\" default=\"$(find-pkg-share FOO_package)/config/foo_node.param.yaml\" />\n\n<node pkg=\"FOO_package\" exec=\"foo_node\">\n...\n <param from=\"$(var foo_node_param_path)\" />\n</node>\n</launch>\nbehavior_path_planner stored under autoware_launchautoware_launch.All the parameter files should have the .param.yaml suffix so that the auto-format can be applied properly.
Warning
Under Construction
"},{"location":"contributing/coding-guidelines/ros-nodes/topic-namespaces/","title":"Topic namespaces","text":""},{"location":"contributing/coding-guidelines/ros-nodes/topic-namespaces/#topic-namespaces","title":"Topic namespaces","text":""},{"location":"contributing/coding-guidelines/ros-nodes/topic-namespaces/#overview","title":"Overview","text":"ROS allows topics, parameters and nodes to be namespaced which provides the following benefits:
This page focuses on how to use namespaces in Autoware and shows some useful examples. For basic information on topic namespaces, refer to this tutorial.
"},{"location":"contributing/coding-guidelines/ros-nodes/topic-namespaces/#how-topics-should-be-named-in-node","title":"How topics should be named in node","text":"Autoware divides the node into the following functional categories, and adds the start namespace for the nodes according to the categories.
When a node is run in a namespace, all topics which that node publishes are given that same namespace. All nodes in the Autoware stack must support namespaces by avoiding practices such as publishing topics in the global namespace.
In general, topics should be namespaced based on the function of the node which produces them and not the node (or nodes) which consume them.
Classify topics as input or output topics based on they are subscribed or published by the node. In the node, input topic is named input/topic_name and output topic is named output/topic_name.
Configure the topic in the node's launch file. Take the joy_controller node as an example, in the following example, set the input and output topics and remap topics in the joy_controller.launch.xml file.
<launch>\n<arg name=\"input_joy\" default=\"/joy\"/>\n<arg name=\"input_odometry\" default=\"/localization/kinematic_state\"/>\n\n<arg name=\"output_control_command\" default=\"/external/$(var external_cmd_source)/joy/control_cmd\"/>\n<arg name=\"output_external_control_command\" default=\"/api/external/set/command/$(var external_cmd_source)/control\"/>\n<arg name=\"output_shift\" default=\"/api/external/set/command/$(var external_cmd_source)/shift\"/>\n<arg name=\"output_turn_signal\" default=\"/api/external/set/command/$(var external_cmd_source)/turn_signal\"/>\n<arg name=\"output_heartbeat\" default=\"/api/external/set/command/$(var external_cmd_source)/heartbeat\"/>\n<arg name=\"output_gate_mode\" default=\"/control/gate_mode_cmd\"/>\n<arg name=\"output_vehicle_engage\" default=\"/vehicle/engage\"/>\n\n<node pkg=\"joy_controller\" exec=\"joy_controller\" name=\"joy_controller\" output=\"screen\">\n<remap from=\"input/joy\" to=\"$(var input_joy)\"/>\n<remap from=\"input/odometry\" to=\"$(var input_odometry)\"/>\n\n<remap from=\"output/control_command\" to=\"$(var output_control_command)\"/>\n<remap from=\"output/external_control_command\" to=\"$(var output_external_control_command)\"/>\n<remap from=\"output/shift\" to=\"$(var output_shift)\"/>\n<remap from=\"output/turn_signal\" to=\"$(var output_turn_signal)\"/>\n<remap from=\"output/gate_mode\" to=\"$(var output_gate_mode)\"/>\n<remap from=\"output/heartbeat\" to=\"$(var output_heartbeat)\"/>\n<remap from=\"output/vehicle_engage\" to=\"$(var output_vehicle_engage)\"/>\n</node>\n</launch>\nHave ~ so that namespace in launch configuration is applied(should not start from root /).
Have ~/input ~/output namespace before topic name used to communicate with other nodes.
e.g., In node obstacle_avoidance_planner, using topic names of type ~/input/topic_name to subscribe to topics.
objects_sub_ = create_subscription<PredictedObjects>(\n\"~/input/objects\", rclcpp::QoS{10},\nstd::bind(&ObstacleAvoidancePlanner::onObjects, this, std::placeholders::_1));\ne.g., In node obstacle_avoidance_planner, using topic names of type ~/output/topic_name to publish topic.
traj_pub_ = create_publisher<Trajectory>(\"~/output/path\", 1);\nVisualization or debug purpose topics should have ~/debug/ namespace.
e.g., In node obstacle_avoidance_planner, in order to debug or visualizing topics, using topic names of type ~/debug/topic_name to publish information.
debug_markers_pub_ =\ncreate_publisher<visualization_msgs::msg::MarkerArray>(\"~/debug/marker\", durable_qos);\n\ndebug_msg_pub_ =\ncreate_publisher<tier4_debug_msgs::msg::StringStamped>(\"~/debug/calculation_time\", 1);\nThe launch configured namespace will be add the topics before, so the topic names will be as following:
/planning/scenario_planning/lane_driving/motion_planning/obstacle_avoidance_planner/debug/marker /planning/scenario_planning/lane_driving/motion_planning/obstacle_avoidance_planner/debug/calculation_time
Rationale: we want to make topic names remapped and configurable from launch files.
Warning
Under Construction
Refer to the following links for now:
Contributions to Autoware's documentation are welcome, and the same principles described in the contribution guidelines should be followed. Small, limited changes can be made by forking this repository and submitting a pull request, but larger changes should be discussed with the community and Autoware maintainers via GitHub Discussion first.
Examples of small changes include:
Examples of larger changes include:
You should refer to the Google developer documentation style guide as much as possible. Reading the Highlights page of that guide is recommended, but if not then the key points below should be noted.
There are two ways to preview your modification on a documentation website.
"},{"location":"contributing/documentation-guidelines/#1-using-github-actions-workflow","title":"1. Using GitHub Actions workflow","text":"Follow the steps below.
deploy-docs label from the sidebar (See below figure).github-actions bot will notify the URL for the pull request's preview.Instead of creating a PR, you can use the mkdocs command to build Autoware's documentation websites on your local computer. Assuming that you are using Ubuntu OS, run the following to install the required libraries.
python3 -m pip install -U $(curl -fsSL https://raw.githubusercontent.com/autowarefoundation/autoware-github-actions/main/deploy-docs/mkdocs-requirements.txt)\nThen, run mkdocs serve on your documentation directory.
cd /PATH/TO/YOUR-autoware-documentation\nmkdocs serve\nIt will launch the MkDocs server. Access http://127.0.0.1:8000/ to see the preview of the website.
"},{"location":"contributing/pull-request-guidelines/","title":"Pull request guidelines","text":""},{"location":"contributing/pull-request-guidelines/#pull-request-guidelines","title":"Pull request guidelines","text":""},{"location":"contributing/pull-request-guidelines/#general-pull-request-workflow","title":"General pull request workflow","text":"Autoware uses the fork-and-pull model. For more details about the model, refer to GitHub Docs.
The following is a general example of the pull request workflow based on the fork-and-pull model. Use this workflow as a reference when you contribute to Autoware.
There are two types of templates. Select one based on the following condition.
maintainer information in package.xml..github/CODEOWNERS file of the repository.@autoware-maintainers.feat(trajectory_follower): add an awesome feature\nNote
You have to start the description part (here add an awesome feature) with a lowercase.
If your change breaks some interfaces, use the ! (breaking changes) mark as follows:
feat(trajectory_follower)!: remove package\nfeat(trajectory_follower)!: change parameter names\nfeat(planning)!: change topic names\nfeat(autoware_utils)!: change function names\nFor the repositories that contain code (most repositories), use the definition of conventional-commit-types for the type.
For documentation repositories such as autoware-documentation, use the following definition:
featfixrefactordocsbuild! (breaking changes)perf and test are generally unused. Other types have the same meaning as the code repositories.
For ROS packages, adding the package name or component name is good.
feat(trajectory_follower): add an awesome feature\nrefactor(planning, control): use common utils\nIt is acceptable if it is agreed with maintainers that there is no other way but to submit a big pull request.
"},{"location":"contributing/pull-request-guidelines/#example_4","title":"Example","text":"feat, fix, refactor, etc.) of changes in the same commit.@{some-of-developers} Would it be possible for you to review this PR?\n@autoware-maintainers friendly ping.\nAutoware has several checks for a pull request. The results are shown at the bottom of the pull request page as below.
If the \u274c mark is shown, click the Details button and investigate the failure reason.
If the Required mark is shown, you cannot merge the pull request unless you resolve the error. If not, it is optional, but preferably it should be fixed.
The following sections explain about common CI checks in Autoware. Note that some repositories may have different settings.
"},{"location":"contributing/pull-request-guidelines/ci-checks/#dco","title":"DCO","text":"The Developer Certificate of Origin (DCO) is a lightweight way for contributors to certify that they wrote or otherwise have the right to submit the code they are contributing to the project.
This workflow checks whether the pull request fulfills DCO. You need to confirm the required items and commit with git commit -s.
For more information, refer to the GitHub App page.
"},{"location":"contributing/pull-request-guidelines/ci-checks/#semantic-pull-request","title":"semantic-pull-request","text":"This workflow checks whether the pull request follows Conventional Commits.
For the detailed rules, see the pull request rules.
"},{"location":"contributing/pull-request-guidelines/ci-checks/#pre-commit","title":"pre-commit","text":"pre-commit is a tool to run formatters or linters when you commit.
This workflow checks whether the pull request has no error with pre-commit.
In the workflow pre-commit.ci - pr is enabled in the repository, it will automatically fix errors by pre-commit.ci as many as possible. If there are some errors remain, fix them manually.
You can run pre-commit in your local environment by the following command:
pre-commit run -a\nOr you can install pre-commit to the repository and automatically run it before committing:
pre-commit install\nSince it is difficult to detect errors with no false positives, some jobs are split into another config file and marked as optional. To check them, use the --config option:
pre-commit run -a --config .pre-commit-config-optional.yaml\nThis workflow detects spelling mistakes using CSpell with our dictionary file. You can submit pull requests to tier4/autoware-spell-check-dict to update the dictionary.
Since it is difficult to detect errors with no false positives, it is an optional workflow, but it is preferable to remove spelling mistakes as many as possible.
"},{"location":"contributing/pull-request-guidelines/ci-checks/#build-and-test-differential","title":"build-and-test-differential","text":"This workflow checks colcon build and colcon test for the pull request. To make the CI faster, it doesn't check all packages but only modified packages and the dependencies.
This workflow is the ARM64 version of build-and-test-differential. You need to add the ARM64 label to run this workflow.
For reference information, since ARM machines are not supported by GitHub-hosted runners, we use self-hosted runners prepared by the AWF. For the details about self-hosted runners, refer to GitHub Docs.
"},{"location":"contributing/pull-request-guidelines/ci-checks/#deploy-docs","title":"deploy-docs","text":"This workflow deploys the preview documentation site for the pull request. You need to add the deploy-docs label to run this workflow.
If there are no corresponding issues, you can ignore this rule.
"},{"location":"contributing/pull-request-guidelines/commit-guidelines/#example","title":"Example","text":"123-add-feature\ndash-case for the separator of branch names (advisory, non-automated)","text":""},{"location":"contributing/pull-request-guidelines/commit-guidelines/#rationale_1","title":"Rationale","text":"123-add-feature\nIf you have already submitted a pull request, you do not have to change the branch name because you need to re-create a pull request, which is noisy and a waste of time. Be careful from the next time.
"},{"location":"contributing/pull-request-guidelines/commit-guidelines/#example_2","title":"Example","text":"Usually it is good to start with a verb.
123-fix-memory-leak-of-trajectory-follower\nDevelopers must certify that they wrote or otherwise have the right to submit the code they are contributing to the project.
"},{"location":"contributing/pull-request-guidelines/commit-guidelines/#rationale_3","title":"Rationale","text":"If not, it will lead to complex license problems.
"},{"location":"contributing/pull-request-guidelines/commit-guidelines/#example_3","title":"Example","text":"git commit -s\nfeat: add a feature\n\nSigned-off-by: Autoware <autoware@example.com>\nWarning
Under Construction
Refer to the following links for now:
There might be some annotations or review comments in the diff view during your review.
To toggle annotations, press the A key.
Before:
After:
To toggle review comments, press the I key.
For other keyboard shortcuts, refer to GitHub Docs.
"},{"location":"contributing/pull-request-guidelines/review-tips/#view-code-in-the-web-based-visual-studio-code","title":"View code in the web-based Visual Studio Code","text":"You can open Visual Studio Code from your browser to view code in a rich UI. To use it, press the . key on any repository or pull request.
For more detailed usage, refer to github/dev.
"},{"location":"contributing/pull-request-guidelines/review-tips/#check-out-the-branch-of-a-pull-request-quickly","title":"Check out the branch of a pull request quickly","text":"If you want to check out the branch of a pull request, it's generally troublesome with the fork-and-pull model.
# Copy the user name and the fork URL.\ngit remote add {user-name} {fork-url}\ngit checkout {user-name}/{branch-name}\ngit remote rm {user-name} # To clean up\nInstead, you can use GitHub CLI to simplify the steps, just run gh pr checkout {pr-number}.
You can copy the command from the top right of the pull request page.
"},{"location":"contributing/testing-guidelines/","title":"Testing guidelines","text":""},{"location":"contributing/testing-guidelines/#testing-guidelines","title":"Testing guidelines","text":""},{"location":"contributing/testing-guidelines/#unit-testing","title":"Unit testing","text":"Unit testing is a software testing method that tests individual units of source code to determine whether they satisfy the specification.
For details, see the Unit testing guidelines.
"},{"location":"contributing/testing-guidelines/#integration-testing","title":"Integration testing","text":"Integration testing combines and tests the individual software modules as a group, and is done after unit testing.
While performing integration testing, the following subtypes of tests are written:
For details, see the Integration testing guidelines.
"},{"location":"contributing/testing-guidelines/integration-testing/","title":"Integration testing","text":""},{"location":"contributing/testing-guidelines/integration-testing/#integration-testing","title":"Integration testing","text":"An integration test is defined as the phase in software testing where individual software modules are combined and tested as a group. Integration tests occur after unit tests, and before validation tests.
The input to an integration test is a set of independent modules that have been unit tested. The set of modules is tested against the defined integration test plan, and the output is a set of properly integrated software modules that is ready for system testing.
"},{"location":"contributing/testing-guidelines/integration-testing/#value-of-integration-testing","title":"Value of integration testing","text":"Integration tests determine if independently developed software modules work correctly when the modules are connected to each other. In ROS 2, the software modules are called nodes. Testing a single node is a special type of integration test that is commonly referred to as component testing.
Integration tests help to find the following types of errors:
malloc failures. This can be tested using tools like stress and udpreplay to test the performance of nodes with real data.With ROS 2, it is possible to program complex autonomous-driving applications with a large number of nodes. Therefore, a lot of effort has been made to provide an integration-test framework that helps developers test the interaction of ROS 2 nodes.
"},{"location":"contributing/testing-guidelines/integration-testing/#integration-test-framework","title":"Integration-test framework","text":"A typical integration-test framework has three parts:
In Autoware, we use the launch_testing framework.
"},{"location":"contributing/testing-guidelines/integration-testing/#smoke-tests","title":"Smoke tests","text":"Autoware has a dedicated API for smoke testing. To use this framework, in package.xml add:
<test_depend>autoware_testing</test_depend>\nAnd in CMakeLists.txt add:
if(BUILD_TESTING)\nfind_package(autoware_testing REQUIRED)\nadd_smoke_test(${PROJECT_NAME} ${NODE_NAME})\nendif()\nDoing so adds smoke tests that ensure that a node can be:
SIGTERM signal.For the full API documentation, refer to the package design page.
Note
This API is not suitable for all smoke test cases. It cannot be used when a specific file location (eg: for a map) is required to be passed to the node, or if some preparation needs to be conducted before node launch. In such cases use the manual solution from the component test section below.
"},{"location":"contributing/testing-guidelines/integration-testing/#integration-test-with-a-single-node-component-test","title":"Integration test with a single node: component test","text":"The simplest scenario is a single node. In this case, the integration test is commonly referred to as a component test.
To add a component test to an existing node, you can follow the example of the lanelet2_map_loader in the map_loader package (added in this PR).
In package.xml, add:
<test_depend>ros_testing</test_depend>\nIn CMakeLists.txt, add or modify the BUILD_TESTING section:
if(BUILD_TESTING)\nadd_ros_test(\ntest/lanelet2_map_loader_launch.test.py\nTIMEOUT \"30\"\n)\ninstall(DIRECTORY\ntest/data/\nDESTINATION share/${PROJECT_NAME}/test/data/\n)\nendif()\nIn addition to the command add_ros_test, we also install any data that is required by the test using the install command.
Note
TIMEOUT argument is given in seconds; see the add_ros_test.cmake file for details.add_ros_test command will run the test in a unique ROS_DOMAIN_ID which avoids interference between tests running in parallel.To create a test, either read the launch_testing quick-start example, or follow the steps below.
Taking test/lanelet2_map_loader_launch.test.py as an example, first dependencies are imported:
import os\nimport unittest\n\nfrom ament_index_python import get_package_share_directory\nimport launch\nfrom launch import LaunchDescription\nfrom launch_ros.actions import Node\nimport launch_testing\nimport pytest\nThen a launch description is created to launch the node under test. Note that the test_map.osm file path is found and passed to the node, something that cannot be done with the smoke testing API:
@pytest.mark.launch_test\ndef generate_test_description():\n\n lanelet2_map_path = os.path.join(\n get_package_share_directory(\"map_loader\"), \"test/data/test_map.osm\"\n )\n\n lanelet2_map_loader = Node(\n package=\"map_loader\",\n executable=\"lanelet2_map_loader\",\n parameters=[{\"lanelet2_map_path\": lanelet2_map_path}],\n )\n\n context = {}\n\n return (\n LaunchDescription(\n [\n lanelet2_map_loader,\n # Start test after 1s - gives time for the map_loader to finish initialization\n launch.actions.TimerAction(\n period=1.0, actions=[launch_testing.actions.ReadyToTest()]\n ),\n ]\n ),\n context,\n )\nNote
TimerAction to delay the start of the test by 1s.context is empty but it can be used to pass objects to the test cases.context in the ROS 2 context_launch_test.py test example.Finally, a test is executed after the node executable has been shut down (post_shutdown_test). Here we ensure that the node was launched without error and exited cleanly.
@launch_testing.post_shutdown_test()\nclass TestProcessOutput(unittest.TestCase):\n def test_exit_code(self, proc_info):\n # Check that process exits with code 0: no error\n launch_testing.asserts.assertExitCodes(proc_info)\nContinuing the example from above, first build your package:
colcon build --packages-up-to map_loader\nsource install/setup.bash\nThen either execute the component test manually:
ros2 test src/universe/autoware.universe/map/map_loader/test/lanelet2_map_loader_launch.test.py\nOr as part of testing the entire package:
colcon test --packages-select map_loader\nVerify that the test is executed; e.g.
$ colcon test-result --all --verbose\n...\nbuild/map_loader/test_results/map_loader/test_lanelet2_map_loader_launch.test.py.xunit.xml: 1 test, 0 errors, 0 failures, 0 skipped\nThe simple test described in Integration test with a single node: component test can be extended in numerous directions, such as testing a node's output.
"},{"location":"contributing/testing-guidelines/integration-testing/#testing-the-output-of-a-node","title":"Testing the output of a node","text":"To test while the node is running, create an active test by adding a subclass of Python's unittest.TestCase to *launch.test.py. Some boilerplate code is required to access output by creating a node and a subscription to a particular topic, e.g.
import unittest\n\nclass TestRunningDataPublisher(unittest.TestCase):\n\n @classmethod\n def setUpClass(cls):\n cls.context = Context()\n rclpy.init(context=cls.context)\n cls.node = rclpy.create_node(\"test_node\", context=cls.context)\n\n @classmethod\n def tearDownClass(cls):\n rclpy.shutdown(context=cls.context)\n\n def setUp(self):\n self.msgs = []\n sub = self.node.create_subscription(\n msg_type=my_msg_type,\n topic=\"/info_test\",\n callback=self._msg_received\n )\n self.addCleanup(self.node.destroy_subscription, sub)\n\n def _msg_received(self, msg):\n # Callback for ROS 2 subscriber used in the test\n self.msgs.append(msg)\n\n def get_message(self):\n startlen = len(self.msgs)\n\n executor = rclpy.executors.SingleThreadedExecutor(context=self.context)\n executor.add_node(self.node)\n\n try:\n # Try up to 60 s to receive messages\n end_time = time.time() + 60.0\n while time.time() < end_time:\n executor.spin_once(timeout_sec=0.1)\n if startlen != len(self.msgs):\n break\n\n self.assertNotEqual(startlen, len(self.msgs))\n return self.msgs[-1]\n finally:\n executor.remove_node(self.node)\n\n def test_message_content():\n msg = self.get_message()\n self.assertEqual(msg, \"Hello, world\")\nUnit testing is the first phase of testing and is used to validate units of source code such as classes and functions. Typically, a unit of code is tested by validating its output for various inputs. Unit testing helps ensure that the code behaves as intended and prevents accidental changes of behavior.
Autoware uses the ament_cmake framework to build and run tests. The same framework is also used to analyze the test results.
ament_cmake provides several convenience functions to make it easy to register tests in a CMake-based package and to ensure that JUnit-compatible result files are generated. It currently supports a few different testing frameworks like pytest, gtest, and gmock.
In order to prevent tests running in parallel from interfering with each other when publishing and subscribing to ROS topics, it is recommended to use commands from ament_cmake_ros to run tests in isolation.
See below for an example of using ament_add_ros_isolated_gtest with colcon test. All other tests follow a similar pattern.
In my_cool_pkg/test, create the gtest code file test_my_cool_pkg.cpp:
#include \"gtest/gtest.h\"\n#include \"my_cool_pkg/my_cool_pkg.hpp\"\nTEST(TestMyCoolPkg, TestHello) {\nEXPECT_EQ(my_cool_pkg::print_hello(), 0);\n}\nIn package.xml, add the following line:
<test_depend>ament_cmake_ros</test_depend>\nNext add an entry under BUILD_TESTING in the CMakeLists.txt to compile the test source files:
if(BUILD_TESTING)\n\nament_add_ros_isolated_gtest(test_my_cool_pkg test/test_my_cool_pkg.cpp)\ntarget_link_libraries(test_my_cool_pkg ${PROJECT_NAME})\n...\nendif()\nThis automatically links the test with the default main function provided by gtest. The code under test is usually in a different CMake target (${PROJECT_NAME} in the example) and its shared object for linking needs to be added.
To register a new gtest item, wrap the test code with the macro TEST (). TEST () is a predefined macro that helps generate the final test code, and also registers a gtest item to be available for execution. The test case name should be in CamelCase, since gtest inserts an underscore between the fixture name and the class case name when creating the test executable.
gtest/gtest.h also contains predefined macros of gtest like ASSERT_TRUE(condition), ASSERT_FALSE(condition), ASSERT_EQ(val1,val2), ASSERT_STREQ(str1,str2), EXPECT_EQ(), etc. ASSERT_* will abort the test if the condition is not satisfied, while EXPECT_* will mark the test as failed but continue on to the next test condition.
Info
More information about gtest and its features can be found in the gtest repo.
In the demo CMakeLists.txt, ament_add_ros_isolated_gtest is a predefined macro in ament_cmake_ros that helps simplify adding gtest code. Details can be viewed in ament_add_gtest.cmake.
By default, all necessary test files (ELF, CTestTestfile.cmake, etc.) are compiled by colcon:
cd ~/workspace/\ncolcon build --packages-select my_cool_pkg\nTest files are generated under ~/workspace/build/my_cool_pkg.
To run all tests for a specific package, call:
$ colcon test --packages-select my_cool_pkg\n\nStarting >>> my_cool_pkg\nFinished <<< my_cool_pkg [7.80s]\n\nSummary: 1 package finished [9.27s]\nThe test command output contains a brief report of all the test results.
To get job-wise information of all executed tests, call:
$ colcon test-result --all\n\nbuild/my_cool_pkg/test_results/my_cool_pkg/copyright.xunit.xml: 8 tests, 0 errors, 0 failures, 0 skipped\nbuild/my_cool_pkg/test_results/my_cool_pkg/cppcheck.xunit.xml: 6 tests, 0 errors, 0 failures, 0 skipped\nbuild/my_cool_pkg/test_results/my_cool_pkg/lint_cmake.xunit.xml: 1 test, 0 errors, 0 failures, 0 skipped\nbuild/my_cool_pkg/test_results/my_cool_pkg/my_cool_pkg_exe_integration_test.xunit.xml: 1 test, 0 errors, 0 failures, 0 skipped\nbuild/my_cool_pkg/test_results/my_cool_pkg/test_my_cool_pkg.gtest.xml: 1 test, 0 errors, 0 failures, 0 skipped\nbuild/my_cool_pkg/test_results/my_cool_pkg/xmllint.xunit.xml: 1 test, 0 errors, 0 failures, 0 skipped\n\nSummary: 18 tests, 0 errors, 0 failures, 0 skipped\nLook in the ~/workspace/log/test_<date>/<package_name> directory for all the raw test commands, std_out, and std_err. There is also the ~/workspace/log/latest_*/ directory containing symbolic links to the most recent package-level build and test output.
To print the tests' details while the tests are being run, use the --event-handlers console_cohesion+ option to print the details directly to the console:
$ colcon test --event-handlers console_cohesion+ --packages-select my_cool_pkg\n\n...\ntest 1\n Start 1: test_my_cool_pkg\n\n1: Test command: /usr/bin/python3 \"-u\" \"~/workspace/install/share/ament_cmake_test/cmake/run_test.py\" \"~/workspace/build/my_cool_pkg/test_results/my_cool_pkg/test_my_cool_pkg.gtest.xml\" \"--package-name\" \"my_cool_pkg\" \"--output-file\" \"~/workspace/build/my_cool_pkg/ament_cmake_gtest/test_my_cool_pkg.txt\" \"--command\" \"~/workspace/build/my_cool_pkg/test_my_cool_pkg\" \"--gtest_output=xml:~/workspace/build/my_cool_pkg/test_results/my_cool_pkg/test_my_cool_pkg.gtest.xml\"\n1: Test timeout computed to be: 60\n1: -- run_test.py: invoking following command in '~/workspace/src/my_cool_pkg':\n1: - ~/workspace/build/my_cool_pkg/test_my_cool_pkg --gtest_output=xml:~/workspace/build/my_cool_pkg/test_results/my_cool_pkg/test_my_cool_pkg.gtest.xml\n1: [==========] Running 1 test from 1 test case.\n1: [----------] Global test environment set-up.\n1: [----------] 1 test from test_my_cool_pkg\n1: [ RUN ] test_my_cool_pkg.test_hello\n1: Hello World\n1: [ OK ] test_my_cool_pkg.test_hello (0 ms)\n1: [----------] 1 test from test_my_cool_pkg (0 ms total)\n1:\n1: [----------] Global test environment tear-down\n1: [==========] 1 test from 1 test case ran. (0 ms total)\n1: [ PASSED ] 1 test.\n1: -- run_test.py: return code 0\n1: -- run_test.py: inject classname prefix into gtest result file '~/workspace/build/my_cool_pkg/test_results/my_cool_pkg/test_my_cool_pkg.gtest.xml'\n1: -- run_test.py: verify result file '~/workspace/build/my_cool_pkg/test_results/my_cool_pkg/test_my_cool_pkg.gtest.xml'\n1/5 Test #1: test_my_cool_pkg ................... Passed 0.09 sec\n\n...\n\n100% tests passed, 0 tests failed out of 5\n\nLabel Time Summary:\ncopyright = 0.49 sec*proc (1 test)\ncppcheck = 0.20 sec*proc (1 test)\ngtest = 0.05 sec*proc (1 test)\nlint_cmake = 0.18 sec*proc (1 test)\nlinter = 1.34 sec*proc (4 tests)\nxmllint = 0.47 sec*proc (1 test)\n\nTotal Test time (real) = 7.91 sec\n...\nLoosely described, a code coverage metric is a measure of how much of the program code has been exercised (covered) during testing.
In the Autoware repositories, Codecov is used to automatically calculate coverage of any open pull request.
More details about the code coverage metrics can be found in the Codecov documentation.
"},{"location":"datasets/","title":"Datasets","text":""},{"location":"datasets/#datasets","title":"Datasets","text":"Autoware partners provide datasets for testing and development. These datasets are available for download here.
"},{"location":"datasets/#bus-odd-operational-design-domain-datasets","title":"Bus-ODD (Operational Design Domain) datasets","text":""},{"location":"datasets/#leo-drive-isuzu-sensor-data","title":"Leo Drive - ISUZU sensor data","text":"This dataset contains data from the Isuzu bus used in the Bus ODD project.
The data contains data from following sensors:
It also contains /tf topic for static transformations between sensors.
The GNSS data is available in sensor_msgs/msg/NavSatFix message type.
But also the Applanix raw messages are also included in applanix_msgs/msg/NavigationPerformanceGsof50 and applanix_msgs/msg/NavigationSolutionGsof49 message types. In order to be able to play back these messages, you need to build and source the applanix_msgs package.
# Create a workspace and clone the repository\nmkdir -p ~/applanix_ws/src && cd \"$_\"\ngit clone https://github.com/autowarefoundation/applanix.git\ncd ..\n\n# Build the workspace\ncolcon build --symlink-install --packages-select applanix_msgs\n\n# Source the workspace\nsource ~/applanix_ws/install/setup.bash\n\n# Now you can play back the messages\nAlso make sure to source Autoware Universe workspace too.
"},{"location":"datasets/#download-instructions","title":"Download instructions","text":"# Install awscli\n$ sudo apt update && sudo apt install awscli -y\n\n# This will download the entire dataset to the current directory.\n# (About 10.9GB of data)\n$ aws s3 sync s3://autoware-files/collected_data/2022-08-22_leo_drive_isuzu_bags/ ./2022-08-22_leo_drive_isuzu_bags --no-sign-request\n\n# Optionally,\n# If you instead want to download a single bag file, you can get a list of the available files with following:\n$ aws s3 ls s3://autoware-files/collected_data/2022-08-22_leo_drive_isuzu_bags/ --no-sign-request\n PRE all-sensors-bag1_compressed/\n PRE all-sensors-bag2_compressed/\n PRE all-sensors-bag3_compressed/\n PRE all-sensors-bag4_compressed/\n PRE all-sensors-bag5_compressed/\n PRE all-sensors-bag6_compressed/\n PRE driving_20_kmh_2022_06_10-16_01_55_compressed/\n PRE driving_30_kmh_2022_06_10-15_47_42_compressed/\n\n# Then you can download a single bag file with the following:\naws s3 sync s3://autoware-files/collected_data/2022-08-22_leo_drive_isuzu_bags/all-sensors-bag1_compressed/ ./all-sensors-bag1_compressed --no-sign-request\nThis dataset contains pcap files and ros2 bag files from Ouster OS1-64 Lidar. The pcap file and ros2 bag file is recorded in the same time with slight difference in duration.
Click here to download (~553MB)
Reference Issue
"},{"location":"design/","title":"Autoware's Design","text":""},{"location":"design/#autowares-design","title":"Autoware's Design","text":""},{"location":"design/#architecture","title":"Architecture","text":"Core and Universe.
Autoware provides the runtimes and technology components by open-source software. The runtimes are based on the Robot Operating System (ROS). The technology components are provided by contributors, which include, but are not limited to:
The downside of the microautonomy architecture is that the computational performance of end applications is sacrificed due to its data path overhead attributed to functional modularity. In other words, the trade-off characteristic of the microautonomy architecture exists between computational performance and functional modularity. This trade-off problem can be solved technically by introducing real-time capability. This is because autonomous driving systems are not really designed to be real-fast, that is, low-latency computing is nice-to-have but not must-have. The must-have feature for autonomous driving systems is that the latency of computing is predictable, that is, the systems are real-time. As a whole, we can compromise computational performance to an extent that is predictable enough to meet the given timing constraints of autonomous driving systems, often referred to as deadlines of computation.
"},{"location":"design/#design","title":"Design","text":"Warning
Under Construction
"},{"location":"design/#autoware-concepts","title":"Autoware concepts","text":"The Autoware concepts page describes the design philosophy of Autoware. Readers (service providers and all Autoware users) will learn the basic concepts underlying Autoware development, such as microautonomy and the Core/Universe architecture.
"},{"location":"design/#autoware-architecture","title":"Autoware architecture","text":"The Autoware architecture page describes an overview of each module that makes up Autoware. Readers (all Autoware users) will gain a high-level picture of how each module that composes Autoware works.
"},{"location":"design/#autoware-interfaces","title":"Autoware interfaces","text":"The Autoware interfaces page describes in detail the interface of each module that makes up Autoware. Readers (intermediate developers) will learn how to add new functionality to Autoware and how to integrate their own modules with Autoware.
"},{"location":"design/#configuration-management","title":"Configuration management","text":""},{"location":"design/#conclusion","title":"Conclusion","text":""},{"location":"design/autoware-architecture/","title":"Architecture overview","text":""},{"location":"design/autoware-architecture/#architecture-overview","title":"Architecture overview","text":"This page describes the architecture of Autoware.
"},{"location":"design/autoware-architecture/#introduction","title":"Introduction","text":"The current Autoware is defined to be a layered architecture that clarifies each module's role and simplifies the interface between them. By doing so:
Note that the initial focus of this architecture design was solely on driving capability, and so the following features were left as future work:
Autoware's architecture consists of the following six stacks. Each linked page contains a more detailed set of requirements and use cases specific to that stack:
A diagram showing Autoware's nodes in the default configuration can be found on the Node diagram page. Detailed documents for each node are available in the Autoware Universe docs.
Note that Autoware configurations are scalable / selectable and will vary depending on the environment and required use cases.
"},{"location":"design/autoware-architecture/#references","title":"References","text":"This document presents the design concept of the Control Component. The content is as follows:
The Control Component generates the control signal to which the Vehicle Component subscribes. The generated control signals are computed based on the reference trajectories from the Planning Component.
The Control Component consists of two modules. The trajectory_follower module generates a vehicle control command to follow the reference trajectory received from the planning module. The command includes, for example, the desired steering angle and target speed. The vehicle_command_gate is responsible for filtering the control command to prevent abnormal values and then sending it to the vehicle. This gate also allows switching between multiple sources such as the MRM (minimal risk maneuver) module or some remote control module, in addition to the trajectory follower.
The Autoware control system is designed as a platform for automated driving systems that can be compatible with a diverse range of vehicles.
The control process in Autoware uses general information (such as target acceleration and deceleration) and no vehicle-specific information (such as brake pressure) is used. Hence it can be adjusted independently of the vehicle's drive interface enabling easy integration or performance tuning.
Furthermore, significant differences that affect vehicle motion constraints, such as two-wheel steering or four-wheel steering, are addressed by switching the control vehicle model, achieving control specialized for each characteristic.
Autoware's control module outputs the necessary information to control the vehicle as a substitute for a human driver. For example, the control command from the control module looks like the following:
- Target steering angle\n- Target steering torque\n- Target speed\n- Target acceleration\nNote that vehicle-specific values such as pedal positions and low-level information such as individual wheel rotation speeds are excluded from the command.
"},{"location":"design/autoware-architecture/control/#vehicle-adaptation-design","title":"Vehicle Adaptation Design","text":""},{"location":"design/autoware-architecture/control/#vehicle-interface-adapter","title":"Vehicle interface adapter","text":"Autoware is designed to be an autonomous driving platform able to accommodate vehicles with various drivetrain types.
This is an explanation of how Autoware handles the standardization of systems with different vehicle drivetrains. The interfaces for vehicle drivetrains are diverse, including steering angle, steering angular velocity, steering torque, speed, accel/brake pedals, and brake pressure. To accommodate these differences, Autoware adds an adapter module between the control component and the vehicle interface. This module performs the conversion between the proprietary message types used by the vehicle (such as brake pressure) and the generic types used by Autoware (such as desired acceleration). By providing this conversion information, the differences in vehicle drivetrain can be accommodated.
If the information is not known in advance, an automatic calibration tool can be used. Calibration will occur within limited degrees of freedom, generating the information necessary for the drivetrain conversion automatically.
This configuration is summarized in the following diagram.
"},{"location":"design/autoware-architecture/control/#examples-of-several-vehicle-interfaces","title":"Examples of several vehicle interfaces","text":"This is an example of the several drivetrain types in the vehicle interface.
Vehicle Lateral interface Longitudinal interface Note Lexus Steering angle Accel/brake pedal position Acceleration lookup table conversion for longitudinal JPN TAXI Steering angle Accel/brake pedal position Acceleration lookup table conversion for longitudinal GSM8 Steering EPS voltage Acceleration motor voltage, Deceleration brake hydraulic pressure lookup table and PID conversion for lateral and longitudinal YMC Golfcart Steering angle Velocity Logiees yaw rate Velocity F1 TENTH Steering angle Motor RPM interface code"},{"location":"design/autoware-architecture/control/#control-feature-design","title":"Control Feature Design","text":"The following lists the features provided by Autoware's Control/Vehicle component, as well as the conditions and assumptions required to utilize them effectively.
The proper operation of the ODD is limited by factors such as whether the functions are enabled, delay time, calibration accuracy and degradation rate, and sensor accuracy.
Feature Description\u3000 Requirements/Assumptions Note \u3000Limitation for now Lateral Control Control the drivetrain system related to lateral vehicle motion Trying to increase the number of vehicle types that can be supported in the future. Only front-steering type is supported. Longitudinal Control Control the drivetrain system related to longitudinal vehicle motion Slope Compensation Supports precise vehicle motion control on slopes Gradient information can be obtained from maps or sensors attached to the chassis If gradient information is not available, the gradient is estimated from the vehicle's pitch angle. Delay Compensation Controls the drivetrain system appropriately in the presence of time delays The drivetrain delay information is provided in advance If there is no delay information, the drivetrain delay is estimated automatically (automatic calibration). However, the effect of delay cannot be completely eliminated, especially in scenarios with sudden changes in speed. Only fixed delay times can be set for longitudinal and lateral drivetrain systems separately. It does not accommodate different delay times for the accelerator and brake. Drivetrain IF Conversion (Lateral Control) Converts the drivetrain-specific information of the vehicle into the drivetrain information used by Autoware (e.g., target steering angular velocity \u2192 steering torque) The conversion information is provided in advance If there is no conversion information, the conversion map is estimated automatically (automatic calibration). The degree of freedom for conversion is limited (2D lookup table + PID FB). Drivetrain IF Conversion (Longitudinal Control) Converts the drivetrain-specific information of the vehicle into the drivetrain information used by Autoware (e.g., target acceleration \u2192 accelerator/brake pedal value) The conversion information is provided in advance If there is no conversion information, the conversion map is estimated automatically (automatic calibration). The degree of freedom for conversion is limited (2D lookup table + PID FB). Automatic Calibration Automatically estimates and applies values such as drivetrain IF conversion map and delay time. The drivetrain status can be obtained (must) Anomaly Detection Notifies when there is a discrepancy in the calibration or unexpected drivetrain behavior The drivetrain status can be obtained (must) Steering Zero Point Correction Corrects the midpoint of the steering to achieve appropriate steering control The drivetrain status can be obtained (must) Steering Deadzone Correction Corrects the deadzone of the steering to achieve appropriate steering control The steering deadzone parameter is provided in advance If the parameter is unknown, the deadzone parameter is estimated from driving information Not available now Steering Deadzone Estimation Dynamically estimates the steering deadzone from driving data Not available now Weight Compensation Performs appropriate vehicle control according to weight Weight information can be obtained from sensors If there is no weight sensor, estimate the weight from driving information. Currently not available Weight Estimation Dynamically estimates weight from driving data Currently not availableThe list above does not cover wheel control systems such as ABS commonly used in vehicles. Regarding these features, the following considerations are taken into account.
"},{"location":"design/autoware-architecture/control/#integration-with-vehicle-side-functions","title":"Integration with vehicle-side functions","text":"ABS (Anti-lock Brake System) and ESC (Electric Stability Control) are two functions that may be pre-installed on a vehicle, directly impacting its controllability. The control modules of Autoware assume that both ABS and ESC are installed on the vehicle and their absence may cause unreliable controls depending on the target ODD. For example, with low-velocity driving in a controlled environment, these functions are not necessary.
Also, note that this statement does not negate the development of ABS functionality in autonomous driving systems.
"},{"location":"design/autoware-architecture/control/#autoware-capabilities-and-vehicle-requirements","title":"Autoware Capabilities and Vehicle Requirements","text":"As an alternative to human driving, autonomous driving systems essentially aim to handle tasks that humans can perform. This includes not only controlling the steering wheel, accel, and brake, but also automatically detecting issues such as poor brake response or a misaligned steering angle. However, this is a trade-off, as better vehicle performance will lead to superior system behavior, ultimately affecting the design of ODD.
On the other hand, for tasks that are not typically anticipated or cannot be handled by a human driver, processing in the vehicle ECU is expected. Examples of such scenarios include cases where the brake response is clearly delayed or when the vehicle rotates due to a single-side tire slipping. These tasks are typically handled by ABS or ESC.
"},{"location":"design/autoware-architecture/localization/","title":"Index","text":"LOCALIZATION COMPONENT DESIGN DOC
"},{"location":"design/autoware-architecture/localization/#abstract","title":"Abstract","text":""},{"location":"design/autoware-architecture/localization/#1-requirements","title":"1. Requirements","text":"Localization aims to estimate vehicle pose, velocity, and acceleration.
Goals:
Non-goals:
This section shows example sensor configurations and their expected performances. Each sensor has its own advantages and disadvantages, but overall performance can be improved by fusing multiple sensors.
"},{"location":"design/autoware-architecture/localization/#3d-lidar-pointcloud-map","title":"3D-LiDAR + PointCloud Map","text":""},{"location":"design/autoware-architecture/localization/#expected-situation","title":"Expected situation","text":"Two architectures are defined, \"Required\" and \"Recommended\". However, the \"Required\" architecture only contains the inputs and outputs necessary to accept various localization algorithms. To improve the reusability of each module, the required components are defined in the \"Recommended\" architecture section along with a more detailed explanation.
"},{"location":"design/autoware-architecture/localization/#required-architecture","title":"Required Architecture","text":""},{"location":"design/autoware-architecture/localization/#input","title":"Input","text":"PoseTwistFusionFilterDevelopers can optionally add other frames such as odom or base_footprint as long as the tf structure above is maintained.
"},{"location":"design/autoware-architecture/localization/#the-localization-modules-ideal-functionality","title":"The localization module's ideal functionality","text":"To maintain sufficient pose estimation performance for safe operation, the following metrics are considered:
For more details about bias, refer to the VectorNav IMU specifications page.\u00a0\u21a9
Autoware relies on high-definition point cloud maps and vector maps of the driving environment to perform various tasks such as localization, route planning, traffic light detection, and predicting the trajectories of pedestrians and other vehicles.
This document describes the design of map component of Autoware, including its requirements, architecture design, features, data formats, and interface to distribute map information to the rest of autonomous driving stack.
"},{"location":"design/autoware-architecture/map/#2-requirements","title":"2. Requirements","text":"Map should provide two types of information to the rest of the stack:
A vector map contains highly accurate information about a road network, lane geometry, and traffic lights. It is required for route planning, traffic light detection, and predicting the trajectories of other vehicles and pedestrians.
A 3D point cloud map is primarily used for LiDAR-based localization and part of perception in Autoware. In order to determine the current position and orientation of the vehicle, a live scan captured from one or more LiDAR units is matched against a pre-generated 3D point cloud map. Therefore, an accurate point cloud map is crucial for good localization results. However, if the vehicle has an alternate localization method with enough accuracy, for example using camera-based localization, point cloud map may not be required to use Autoware.
"},{"location":"design/autoware-architecture/map/#3-architecture","title":"3. Architecture","text":"Warning
Under Construction
"},{"location":"design/autoware-architecture/map/#4-features","title":"4. Features","text":"Warning
Under Construction
"},{"location":"design/autoware-architecture/map/#5-map-specification","title":"5. Map Specification","text":""},{"location":"design/autoware-architecture/map/#point-cloud-map","title":"Point Cloud Map","text":"The point cloud map must be supplied as a file with the following requirements:
Note
Three global coordinate systems are currently supported by Autoware, including Military Grid Reference System (MGRS), Universal Transverse Mercator (UTM), and Japan Rectangular Coordinate System. However, MGRS is a preferred coordinate system for georeferenced maps. In a map with MGRS coordinate system, the X and Y coordinates of each point represent the point's location within the 100,000-meter square, while the Z coordinate represents the point's elevation.
If it is split into a single file, Autoware assumes the following directory structure by default.
sample-map-rosbag\n\u251c\u2500\u2500 lanelet2_map.osm\n\u251c\u2500\u2500 pointcloud_map.pcd\nIf it is split into multiple files, Autoware assumes the following directory structure by default.
sample-map-rosbag\n\u251c\u2500\u2500 lanelet2_map.osm\n\u251c\u2500\u2500 pointcloud_map\n\u251c\u2500\u2500 pcd_00.pcd\n\u251c\u2500\u2500 pcd_01.pcd\n\u251c\u2500\u2500 pcd_02.pcd\n\u251c\u2500\u2500 ...\n\u2514\u2500\u2500 pointcloud_map_metadata.yaml\nNote that, if you split the map into multiple files, you must meet the following additional conditions:
Metadata should look like as follows:
x_resolution: 100.0\ny_resolution: 150.0\nA.pcd: [1200, 2500] # -> 1200 < x < 1300, 2500 < y < 2650\nB.pcd: [1300, 2500] # -> 1300 < x < 1400, 2500 < y < 2650\nC.pcd: [1200, 2650] # -> 1200 < x < 1300, 2650 < y < 2800\nD.pcd: [1400, 2650] # -> 1400 < x < 1500, 2650 < y < 2800\nYou may use pointcloud_divider from MAP IV for dividing pointcloud map as well as generating the compatible metadata.yaml.
"},{"location":"design/autoware-architecture/map/#vector-map","title":"Vector Map","text":"The vector cloud map must be supplied as a file with the following requirements:
Warning
Under Construction
"},{"location":"design/autoware-architecture/node-diagram/","title":"Node diagram","text":""},{"location":"design/autoware-architecture/node-diagram/#node-diagram","title":"Node diagram","text":"This page depicts the node diagram designs for Autoware Core/Universe architecture.
"},{"location":"design/autoware-architecture/node-diagram/#autoware-core","title":"Autoware Core","text":"TBD.
"},{"location":"design/autoware-architecture/node-diagram/#autoware-universe","title":"Autoware Universe","text":"Open in draw.io for fullscreen
Note that the diagram is for reference. We are planning to update this diagram every release and may have old information between the releases. If you wish to check the latest node diagram use rqt_graph after launching the Autoware.
"},{"location":"design/autoware-architecture/perception/","title":"Perception Component design","text":""},{"location":"design/autoware-architecture/perception/#perception-component-design","title":"Perception Component design","text":""},{"location":"design/autoware-architecture/perception/#purpose-of-this-document","title":"Purpose of this document","text":"This document outlines the high-level design strategies, goals and related rationales in the development of the Perception Component. Through this document, it is expected that all OSS developers will comprehend the design philosophy, goals and constraints under which the Perception Component is designed, and participate seamlessly in the development.
"},{"location":"design/autoware-architecture/perception/#overview","title":"Overview","text":"The Perception Component receives inputs from Sensing, Localization, and Map components, and adds semantic information (e.g., Object Recognition, Obstacle Segmentation, Traffic Light Recognition, Occupancy Grid Map), which is then passed on to Planning Component. This component design follows the overarching philosophy of Autoware, defined as the microautonomy concept.
"},{"location":"design/autoware-architecture/perception/#goals-and-non-goals","title":"Goals and non-goals","text":"The role of the Perception Component is to recognize the surrounding environment based on the data obtained through Sensing and acquire sufficient information (such as the presence of dynamic objects, stationary obstacles, blind spots, and traffic signal information) to enable autonomous driving.
In our overall design, we emphasize the concept of microautonomy architecture. This term refers to a design approach that focuses on the proper modularization of functions, clear definition of interfaces between these modules, and as a result, high expandability of the system. Given this context, the goal of the Perception Component is set not to solve every conceivable complex use case (although we do aim to support basic ones), but rather to provide a platform that can be customized to the user's needs and can facilitate the development of additional features.
To clarify the design concepts, the following points are listed as goals and non-goals.
Goals:
Non-goals:
This diagram describes the high-level architecture of the Perception Component.
The Perception Component consists of the following sub-components:
The following describes the input/output concept between Perception Component and other components. See the Perception Component Interface (WIP) page for the current implementation.
"},{"location":"design/autoware-architecture/perception/#input-to-the-perception-component","title":"Input to the Perception Component","text":"As mentioned in the goal session, this perception module is designed to be extensible by third-party components. For specific instructions on how to add new modules and expand its functionality, please refer to the provided documentation or guidelines (WIP).
"},{"location":"design/autoware-architecture/perception/#supported-functions","title":"Supported Functions","text":"Feature Description Requirements LiDAR DNN based 3D detector This module takes point clouds as input and performs detection of objects such as vehicles, trucks, buses, pedestrians, and bicycles. - Point Clouds Camera DNN based 2D detector This module takes camera image as input and performs detection of objects such as vehicles, trucks, buses, pedestrians, and bicycles. - Camera Images LiDAR Clustering This module performs clustering of point clouds and shape estimation to achieve object detection without labels. - Point Clouds Semi-rule based detector This module performs object detection using information from both images and point clouds, and it consists of two components: LiDAR Clustering and Camera DNN based 2D detector. - Output from Camera DNN based 2D detector and LiDAR Clustering Object Merger This module integrates results from various detectors. - Detected Objects Interpolator This module stabilizes the object detection results by maintaining long-term detection results using Tracking results. - Detected Objects - Tracked Objects Tracking This module gives ID and estimate velocity to the detection results. - Detected Objects Prediction This module predicts the future paths (and their probabilities) of dynamic objects according to the shape of the map and the surrounding environment. - Tracked Objects - Vector Map Obstacle Segmentation This module identifies point clouds originating from obstacles that the ego vehicle should avoid. - Point Clouds - Point Cloud Map Occupancy Grid Map This module detects blind spots (areas where no information is available and where dynamic objects may jump out). - Point Clouds - Point Cloud Map Traffic Light Recognition This module detects the position and state of traffic signals. - Camera Images - Vector Map"},{"location":"design/autoware-architecture/perception/#reference-implementation","title":"Reference Implementation","text":"When Autoware is launched, the default parameters are loaded, and the Reference Implementation is started. For more details, please refer to the Reference Implementation.
"},{"location":"design/autoware-architecture/perception/reference_implementation/","title":"Perception component Reference Implementation Design","text":""},{"location":"design/autoware-architecture/perception/reference_implementation/#perception-component-reference-implementation-design","title":"Perception component Reference Implementation Design","text":""},{"location":"design/autoware-architecture/perception/reference_implementation/#purpose-of-this-document","title":"Purpose of this document","text":"This document outlines detailed design of the reference imprementations. This allows developers and users to understand what is currently available with the Perception Component, how to utilize, expand, or add to its features.
"},{"location":"design/autoware-architecture/perception/reference_implementation/#architecture","title":"Architecture","text":"This diagram describes the architecture of the reference implementation.
The Perception component consists of the following sub-components:
The Planning component generates the trajectory message that will be subscribed to by the Control component based on the environmental state obtained from the Localization and the Perception components.
"},{"location":"design/autoware-architecture/planning/#requirements","title":"Requirements","text":"The goal of the Planning component is to generate a trajectory (path and velocity) of the ego vehicle that is safe and well-regulated while satisfying the given mission.
Goals:
Non-goals:
This diagram describes the high-level architecture of the Planning Component.
The Planning component consists of the following sub-components:
Each component contains some modules that can be dynamically loaded and unloaded based on the situation. For instance, the Behavior Planning component includes modules such as lane change, intersection, and crosswalk modules.
Our planning components are built based on the microautonomy architecture with Autoware. We adopt a modular system framework where the tasks are implemented as modules that can be dynamically loaded and unloaded to achieve different features depending on the given use cases.
"},{"location":"design/autoware-architecture/planning/#component-interface","title":"Component interface","text":"The following describes the input/output concept between Planning Component and other components. See the Planning Component Interface (WIP) page for the current implementation.
"},{"location":"design/autoware-architecture/planning/#input-to-the-planning-component","title":"Input to the planning component","text":"As mentioned in the goal session, this planning module is designed to be extensible by third-party components. For specific instructions on how to add new modules and expand its functionality, please refer to the provided documentation or guidelines (WIP).
"},{"location":"design/autoware-architecture/planning/#supported-functions","title":"Supported Functions","text":"Feature Description Requirements Figure Route Planning Plan route from the ego vehicle position to the destination. Reference implementation is in Mission Planner, enabled by launching themission_planner node. - Lanelet map (driving lanelets) Path Planning from Route Plan path to be followed from the given route. Reference implementation is in Behavior Path Planner. - Lanelet map (driving lanelets) Obstacle Avoidance Plan path to avoid obstacles by steering operation. Reference implementation is in Avoidance, Obstacle Avoidance Planner. Enable flag in parameter: launch obstacle_avoidance_planner true - objects information Path Smoothing Plan path to achieve smooth steering. Reference implementation is in Obstacle Avoidance Planner. - Lanelet map (driving lanelet) Narrow Space Driving Plan path to drive within the drivable area. Furthermore, when it is not possible to drive within the drivable area, stop the vehicle to avoid exiting the drivable area. Reference implementation is in Obstacle Avoidance Planner. - Lanelet map (high-precision lane boundaries) Lane Change Plan path for lane change to reach the destination. Reference implementation is in Lane Change.. Enable flag in both parameters: - Lanelet map (driving lanelets) Pull Over Plan path for pull over to park at the road shoulder. Reference implementation is in Goal Planner. - Lanelet map (shoulder lane) Pull Out Plan path for pull over to start from the road shoulder. Reference implementation is in Pull Out Module. - Lanelet map (shoulder lane) Path Shift Plan path in lateral direction in response to external instructions. Reference implementation is in Side Shift Module. - None Obstacle Stop Plan velocity to stop for an obstacle on the path. Reference implementation is in Obstacle Stop Planner, Obstacle Cruise Planner. launch obstacle_stop_planner and enable flag: TODO, launch obstacle_cruise_planner and enable flag: TODO - objects information Obstacle Deceleration Plan velocity to decelerate for an obstacle located around the path. Reference implementation is in Obstacle Stop Planner, Obstacle Cruise Planner. - objects information Adaptive Cruise Control Plan velocity to follow the vehicle driving in front of the ego vehicle. Reference implementation is in Obstacle Stop Planner, Obstacle Cruise Planner. - objects information Decelerate for cut-in vehicles Plan velocity to avoid a risk for cutting-in vehicle to ego lane. Reference implementation is in Obstacle Cruise Planner. - objects information Surround Check at starting Plan velocity to prevent moving when an obstacle exists around the vehicle. Reference implementation is in Surround Obstacle Checker. - objects information Curve Deceleration Plan velocity to decelerate the speed on a curve. Reference implementation is in Motion Velocity Smoother. - None Curve Deceleration for Obstacle Plan velocity to decelerate the speed on a curve for a risk of obstacle collision around the path. Reference implementation is in Obstacle Velocity Limiter. - objects information - Lanelet map (static obstacle) Crosswalk Plan velocity to stop or decelerate for pedestrians approaching or walking on a crosswalk. Reference implementation is in Crosswalk Module. - objects information - Lanelet map (pedestrian crossing) Intersection Oncoming Vehicle Check Plan velocity for turning right/left at intersection to avoid a risk with oncoming other vehicles. Reference implementation is in Intersection Module. - objects information - Lanelet map (intersection lane and yield lane) Intersection Blind Spot Check Plan velocity for turning right/left at intersection to avoid a risk with other vehicles or motorcycles coming from behind blind spot. Reference implementation is in Intersection Module. - objects information - Lanelet map (intersection lane) Intersection Occlusion Check Plan velocity for turning right/left at intersection to avoid a risk with the possibility of coming vehicles from occlusion area. Reference implementation is in Intersection Module. - objects information - Lanelet map (intersection lane) WIP Intersection Traffic Jam Detection Plan velocity for intersection not to enter the intersection when a vehicle is stopped ahead for a traffic jam. Reference implementation is in Intersection Module. - objects information - Lanelet map (intersection lane) Traffic Light Plan velocity for intersection according to a traffic light signal. Reference implementation is in Traffic Light Module. - Traffic light color information Run-out Check Plan velocity to decelerate for the possibility of nearby objects running out into the path. Reference implementation is in Run Out Module. - objects information Stop Line Plan velocity to stop at a stop line. Reference implementation is in Stop Line Module. - Lanelet map (stop line) Occlusion Spot Check Plan velocity to decelerate for objects running out from occlusion area, for example, from behind a large vehicle. Reference implementation is in Occlusion Spot Module. - objects information - Lanelet map (private/public lane) No Stop Area Plan velocity not to stop in areas where stopping is prohibited, such as in front of the fire station entrance. Reference implementation is in No Stopping Area Module. - Lanelet map (no stopping area) Merge from Private Area to Public Road Plan velocity for entering the public road from a private driveway to avoid a risk of collision with pedestrians or other vehicles. Reference implementation is in Merge from Private Area Module. - objects information - Lanelet map (private/public lane) WIP Speed Bump Plan velocity to decelerate for speed bumps. Reference implementation is in Speed Bump Module. - Lanelet map (speed bump) WIP Detection Area Plan velocity to stop at the corresponding stop when an object exist in the designated detection area. Reference implementation is in Detection Area Module. - Lanelet map (detection area) Out of ODD area Plan velocity to stop before exiting the area designated by ODD (Operational Design Domain). Reference implementation is in (WIP). - Lanelet map (invalid lanelet) WIP Collision Detection when deviating from lane Plan velocity to avoid conflict with other vehicles driving in the another lane when the ego vehicle is deviating from own lane. Reference implementation is in Out of Lane Module. - objects information - Lanelet map (driving lane) WIP Parking Plan path and velocity for given goal in parking area. Reference implementation is in Free Space Planner. - objects information - Lanelet map (parking area) Autonomous Emergency Braking (AEB) Perform an emergency stop if a collision with an object ahead is anticipated. It is noted that this function is expected as a final safety layer, and this should work even in the event of failures in the Localization or Perception system. Reference implementation is in Out of Lane Module. - Primitive objects WIP Minimum Risk Maneuver (MRM) Provide appropriate MRM (Minimum Risk Maneuver) instructions when a hazardous event occurs. For example, when a sensor trouble found, send an instruction for emergency braking, moderate stop, or pulling over to the shoulder, depending on the severity of the situation. Reference implementation is in TODO - TODO WIP Trajectory Validation Check the planned trajectory is safe. If it is unsafe, take appropriate action, such as modify the trajectory, stop sending the trajectory or report to the autonomous driving system. Reference implementation is in Planning Validator. - None WIP Running Lane Map Generation Generate lane map from localization data recorded in manual driving. Reference implementation is in WIP - None WIP Running Lane Optimization Optimize the centerline (reference path) of the map to make it smooth considering the vehicle kinematics. Reference implementation is in Static Centerline Optimizer. - Lanelet map (driving lanes) WIP"},{"location":"design/autoware-architecture/planning/#reference-implementation","title":"Reference Implementation","text":"The following diagram describes the reference implementation of the Planning component. By adding new modules or extending the functionalities, various ODDs can be supported.
Note that some implementation does not adhere to the high-level architecture design and require updating.
For more details, please refer to the design documents in each package.
obstacle_stop_planner stop_planner.stop_position.max_longitudinal_margin double distance between the ego and the front vehicle when stopping (when cruise_planner_type:=obstacle_stop_planner) obstacle_cruise_planner common.safe_distance_margin double distance between the ego and the front vehicle when stopping (when cruise_planner_type:=obstacle_cruise_planner) behavior_path_planner avoidance.avoidance.lateral.lateral_collision_margin double minimum lateral margin to obstacle on avoidance behavior_path_planner avoidance.avoidance.lateral.lateral_collision_safety_buffer double additional lateral margin to obstacle if possible on avoidance obstacle_avoidance_planner option.enable_outside_drivable_area_stop bool If set true, a stop point will be inserted before the path footprint is outside the drivable area."},{"location":"design/autoware-architecture/planning/#notation","title":"Notation","text":""},{"location":"design/autoware-architecture/planning/#1-self-crossing-road-and-overlapped","title":"[1] self-crossing road and overlapped","text":"To support the self-crossing road and overlapped road in the opposite direction, each planning module has to meet the specifications
Currently, the supported modules are as follows.
Some functions do not support paths with only one point. Therefore, each modules should generate the path with more than two path points.
"},{"location":"design/autoware-architecture/sensing/","title":"Sensing component design","text":""},{"location":"design/autoware-architecture/sensing/#sensing-component-design","title":"Sensing component design","text":""},{"location":"design/autoware-architecture/sensing/#overview","title":"Overview","text":"Sensing component is a collection of modules that apply some primitive pre-processing to the raw sensor data.
The sensor input formats are defined in this component.
"},{"location":"design/autoware-architecture/sensing/#role","title":"Role","text":"Warning
Under Construction
"},{"location":"design/autoware-architecture/sensing/data-types/image/","title":"Image pre-processing design","text":""},{"location":"design/autoware-architecture/sensing/data-types/image/#image-pre-processing-design","title":"Image pre-processing design","text":"Warning
Under Construction
"},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/","title":"Point cloud pre-processing design","text":""},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/#point-cloud-pre-processing-design","title":"Point cloud pre-processing design","text":""},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/#overview","title":"Overview","text":"Point cloud pre-processing is a collection of modules that apply some primitive pre-processing to the raw sensor data.
This pipeline covers the flow of data from drivers to the perception stack.
"},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/#recommended-processing-pipeline","title":"Recommended processing pipeline","text":"graph TD\n Driver[\"Lidar Driver\"] -->|\"Cloud XYZIRCADT\"| FilterPR[\"Polygon Remover Filter / CropBox Filter\"]\n\n subgraph \"sensing\"\n FilterPR -->|\"Cloud XYZIRCADT\"| FilterDC[\"Motion Distortion Corrector Filter\"]\n FilterDC -->|\"Cloud XYZIRCAD\"| FilterOF[\"Outlier Remover Filter\"]\n FilterOF -->|\"Cloud XYZIRC\"| FilterDS[\"Downsampler Filter\"]\n FilterDS -->|\"Cloud XYZIRC\"| FilterTrans[\"Cloud Transformer\"]\n FilterTrans -->|\"Cloud XYZIRC\"| FilterC\n\n FilterX[\"...\"] -->|\"Cloud XYZIRC (i)\"| FilterC[\"Cloud Concatenator\"]\n end\n\n FilterC -->|\"Cloud XYZIRC\"| SegGr[\"Ground Segmentation\"]The modules used here are from pointcloud_preprocessor package.
For details about the modules, see the following table.
It is recommended that these modules are used in a single container as components. For details see ROS 2 Composition
"},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/#point-cloud-fields","title":"Point cloud fields","text":"In the ideal case, the driver is expected to output a point cloud with the PointXYZIRCADT point type.
X FLOAT32 false X position Y FLOAT32 false Y position Z FLOAT32 false Z position I (intensity) UINT8 false Measured reflectivity, intensity of the point R (return type) UINT8 false Laser return type for dual return lidars C (channel) UINT16 false Vertical channel id of the laser that measured the point A (azimuth) FLOAT32 true atan2(Y, X), Horizontal angle from the front of the lidar to the point D (distance) FLOAT32 true hypot(X, Y, Z), Euclidean distance of the point to lidar T (time) UINT32 false Nanoseconds passed since the time of the header when this point was measured Note
A (azimuth) and D (distance) fields are derived fields. They are provided by the driver to reduce the computational load on some parts of the perception stack.
Note
If the Motion Distortion Corrector Filter won't be used, the T (time) field can be omitted, PointXYZIRCAD point type can be used.
Warning
Autoware will support conversion from PointXYZI to PointXYZIRC or PointXYZIRCAD (with channel and return is set to 0) for prototyping purposes. However, this conversion is not recommended for production use since it's not efficient.
We will use following ranges for intensity, compatible with the VLP16 User Manual:
Quoting from the VLP-16 User Manual:
For each laser measurement, a reflectivity byte is returned in addition to distance. Reflectivity byte values are segmented into two ranges, allowing software to distinguish diffuse reflectors (e.g. tree trunks, clothing) in the low range from retroreflectors (e.g. road signs, license plates) in the high range. A retroreflector reflects light back to its source with a minimum of scattering. The VLP-16 provides its own light, with negligible separation between transmitting laser and receiving detector, so retroreflecting surfaces pop with reflected IR light compared to diffuse reflectors that tend to scatter reflected energy.
In a typical point cloud without retroreflectors, all intensity points will be between 0 and 100.
Retroreflective Gradient road sign, Image Source
But in a point cloud with retroreflectors, the intensity points will be between 0 and 255.
"},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/#intensity-mapping-for-other-lidar-brands","title":"Intensity mapping for other lidar brands","text":""},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/#hesai-pandarxt16","title":"Hesai PandarXT16","text":"Hesai Pandar XT16 User Manual
This lidar has 2 modes for reporting reflectivity:
If you are using linear mapping mode, you should map from [0, 255] to [0, 100] when constructing the point cloud.
If you are using non-linear mapping mode, you should map (hesai to autoware)
when constructing the point cloud.
"},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/#livox-mid-70","title":"Livox Mid-70","text":"Livox Mid-70 User Manual
This lidar has 2 modes for reporting reflectivity similar to Velodyne VLP-16, only the ranges are slightly different.
You should map (livox to autoware)
when constructing the point cloud.
"},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/#robosense-rs-lidar-16","title":"RoboSense RS-LiDAR-16","text":"RoboSense RS-LiDAR-16 User Manual
No mapping required, same as Velodyne VLP-16.
"},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/#ouster-os-1-64","title":"Ouster OS-1-64","text":"Software User Manual v2.0.0 for all Ouster sensors
In the manual it is stated:
Reflectivity [16 bit unsigned int] - sensor Signal Photons measurements are scaled based on measured range and sensor sensitivity at that range, providing an indication of target reflectivity. Calibration of this measurement has not currently been rigorously implemented, but this will be updated in a future firmware release.
So it is advised to map the 16 bit reflectivity to [0, 100] range.
"},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/#leishen-ch64w","title":"Leishen CH64W","text":"I couldn't get the english user manual, link of website
In a user manual I was able to find it says:
Byte 7 represents echo strength, and the value range is 0-255. (Echo strength can reflect the energy reflection characteristics of the measured object in the actual measurement environment. Therefore, the echo strength can be used to distinguish objects with different reflection characteristics.)
So it is advised to map the [0, 255] to [0, 100] range.
"},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/#return-type","title":"Return type","text":"Various lidars support multiple return modes. Velodyne lidars support Strongest and Last return modes.
In the PointXYZIRCT and PointXYZIRC types, R field represents return mode with an UINT8.
0 Unknown / Not Marked 1 Strongest 2 Last"},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/#channel","title":"Channel","text":"The channel field is used to identify the vertical channel of the laser that measured the point. In various lidar manuals or literature, it can also be called laser id, ring, laser line.
For Velodyne VLP-16, there are 16 channels. Default order of channels in drivers are generally in firing order.
In the PointXYZIRCT and PointXYZIRC types, C field represents the vertical channel id with an UINT16.
Warning
This section is subject to change. Following are suggestions and open for discussion.
For solid state lidars that have lines, assign row number as the channel id.
For petal pattern lidars, you can keep channel 0.
"},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/#time","title":"Time","text":"In lidar point clouds, each point measurement can have its individual time stamp. This information can be used to eliminate the motion blur that is caused by the movement of the lidar during the scan.
"},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/#point-cloud-header-time","title":"Point cloud header time","text":"The header contains a Time field. The time field has 2 components:
Field Type Descriptionsec int32 Unix time (seconds elapsed since January 1, 1970) nanosec uint32 Nanoseconds elapsed since the sec field The header of the point cloud message is expected to have the time of the earliest point it has.
Note
The sec field is int32 in ROS 2 humble. The largest value it can represent is 2^31 seconds, it is subject to year 2038 problems. We will wait for actions on ROS 2 community side.
More info at: https://github.com/ros2/rcl_interfaces/issues/85
"},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/#individual-point-time","title":"Individual point time","text":"Each PointXYZIRCT point type has the T field for representing the nanoseconds passed since the first-shot point of the point cloud.
To calculate exact time each point was shot, the T nanoseconds are added to the header time.
Note
The T field is uint32 type. The largest value it can represent is 2^32 nanoseconds, which equates to roughly 4.29 seconds. Usual point clouds don't last more than 100ms for full cycle. So this field should be enough.
Warning
Under Construction
"},{"location":"design/autoware-architecture/sensing/data-types/ultrasonics-data/","title":"Ultrasonics data pre-processing design","text":""},{"location":"design/autoware-architecture/sensing/data-types/ultrasonics-data/#ultrasonics-data-pre-processing-design","title":"Ultrasonics data pre-processing design","text":"Warning
Under Construction
"},{"location":"design/autoware-architecture/vehicle/","title":"Vehicle Interface design","text":""},{"location":"design/autoware-architecture/vehicle/#vehicle-interface-design","title":"Vehicle Interface design","text":""},{"location":"design/autoware-architecture/vehicle/#abstract","title":"Abstract","text":"The Vehicle Interface component provides an interface between Autoware and a vehicle that passes control signals to the vehicle\u2019s drive-by-wire system and receives vehicle information that is passed back to Autoware.
"},{"location":"design/autoware-architecture/vehicle/#1-requirements","title":"1. Requirements","text":"Goals:
Non-goals:
The Vehicle Interface component consists of the following components:
Each component contains static nodes of Autoware, while each module can be dynamically loaded and unloaded (corresponding to C++ classes). The mechanism of the Vehicle Interface component is depicted by the following figures:
"},{"location":"design/autoware-architecture/vehicle/#3-features","title":"3. Features","text":"The Vehicle Interface component can provide the following features in functionality and capability:
Basic functions
Additional functionality and capability features may be added, depending on the vehicle hardware. Some example features are listed below:
The interface of the Vehicle Interface component for other components running in the same process space to access the functionality and capability of the Vehicle Interface component is defined as follows.
From Control
From Planning
From the vehicle
The output interface of the Vehicle Interface component:
The data structure for the internal representation of semantics for the objects and trajectories used in the Vehicle Interface component is defined as follows:
"},{"location":"design/autoware-architecture/vehicle/#5-concerns-assumptions-and-limitations","title":"5. Concerns, Assumptions, and Limitations","text":"Concerns
Assumptions
-
Limitations
"},{"location":"design/autoware-architecture/vehicle/#6-examples-of-accuracy-requirements-by-odd","title":"6. Examples of accuracy requirements by ODD","text":""},{"location":"design/autoware-concepts/","title":"Autoware concepts","text":""},{"location":"design/autoware-concepts/#autoware-concepts","title":"Autoware concepts","text":"Autoware is the world\u2019s first open-source software for autonomous driving systems. Autoware provides value for both The technology developers of autonomous driving systems can create new components based on Autoware. The service operators of autonomous driving systems, on the other hand, can select appropriate technology components with Autoware. This is enabled by the microautonomy architecture that modularizes its software stack into the core and universe subsystems (modules).
"},{"location":"design/autoware-concepts/#microautonomy-architecture","title":"Microautonomy architecture","text":"Autoware uses a pipeline architecture to enable the development of autonomous driving systems. The pipeline architecture used in Autoware consists of components similar to three-layer-architecture. And they run in parallel. There are 2 main modules: the Core and the Universe. The components in these modules are designed to be extensible and reusable. And we call it microautonomy architecture.
"},{"location":"design/autoware-concepts/#the-core-module","title":"The Core module","text":"The Core module contains basic runtimes and technology components that satisfy the basic functionality and capability of sensing, computing, and actuation required for autonomous driving systems. AWF develops and maintains the Core module with their architects and leading members through their working groups. Anyone can contribute to the Core but the PR(Pull Request) acceptance criteria is more strict compared to the Universe.
"},{"location":"design/autoware-concepts/#the-universe-module","title":"The Universe module","text":"The Universe modules are extensions to the Core module that can be provided by the technology developers to enhance the functionality and capability of sensing, computing, and actuation. AWF provides the base Universe module to extend from. A key feature of the microautonomy architecture is that the Universe modules can be contributed to by any organization and individual. That is, you can even create your Universe and make it available for the Autoware community and ecosystem. AWF is responsible for quality control of the Universe modules through their development process. As a result, there are multiple types of the Universe modules - some are verified and validated by AWF and others are not. It is up to the users of Autoware which Universe modules are selected and integrated to build their end applications.
"},{"location":"design/autoware-concepts/#interface-design","title":"Interface design","text":"The interface design is the most essential piece of the microautonomy architecture, which is classified into internal and external interfaces. The component interface is designed for the components in a Universe module to communicate with those in other modules, including the Core module, within Autoware internally. The AD(Autonomous Driving) API, on the other hand, is designed for the applications of Autoware to access the technology components in the Core and Universe modules of Autoware externally. Designing solid interfaces, the microautonomy architecture is made possible with AWF's partners, and at the same time is made feasible for the partners.
"},{"location":"design/autoware-concepts/#challenges","title":"Challenges","text":"A grand challenge of the microautonomy architecture is to achieve real-time capability, which guarantees all the technology components activated in the system to predictably meet timing constraints (given deadlines). In general, it is difficult, if not impossible, to tightly estimate the worst-case execution times (WCETs) of components.
In addition, it is also difficult, if not impossible, to tightly estimate the end-to-end latency of components connected by a DAG. Autonomous driving systems based on the microautonomy architecture, therefore, must be designed to be fail-safe but not never-fail. We accept that the timing constraints may be violated (the given deadlines may be missed) as far as the overrun is taken into account. The overrun handlers are two-fold: (i) platform-defined and (ii) user-defined. The platform-defined handler is implemented as part of the platform by default, while the user-defined handler can overwrite it or add a new handler to the system. This is what we call \u201cfail-safe\u201d on a timely basis.
"},{"location":"design/autoware-concepts/#requirements-and-roadmap","title":"Requirements and roadmap","text":"Goals:
Autoware is the world's first \"all-in-one\" open-source software for self-driving vehicles. Since it was first released in 2015, there have been multiple releases made with differing underlying concepts, each one aimed at improving the software.
"},{"location":"design/autoware-concepts/difference-from-ai-and-auto/#autowareai","title":"Autoware.AI","text":"Autoware.AI is the first distribution of Autoware that was released based on ROS 1. The repository contains a variety of packages covering different aspects of autonomous driving technologies - sensing, actuation, localization, mapping, perception and planning.
While it was successful in attracting many developers and contributions, it was difficult to improve Autoware.AI's capabilities for a number of reasons:
Furthermore, there was no clear definition of the conditions under which an Autoware-enabled autonomous vehicle could operate, nor of the use cases or situations supported (eg: the ability to overtake a stationary vehicle).
From the lessons learned from Autoware.AI development, a different development process was taken for Autoware.Auto to develop a ROS 2 version of Autoware.
Warning
Autoware.AI is currently in maintenance mode and will reach end-of-life at the end of 2022.
"},{"location":"design/autoware-concepts/difference-from-ai-and-auto/#autowareauto","title":"Autoware.Auto","text":"Autoware.Auto is the second distribution of Autoware that was released based on ROS 2. As part of the transition to ROS 2, it was decided to avoid simply porting Autoware.AI from ROS 1 to ROS 2. Instead, the codebase was rewritten from scratch with proper engineering practices, including defining target use cases and ODDs (eg: Autonomous Valet Parking [AVP], Cargo Delivery, etc.), designing a proper architecture, writing design documents and test code.
Autoware.Auto development seemed to work fine initially, but after completing the AVP and and Cargo Delivery ODD projects, we started to see the following issues:
In order to address the issues with Autoware.Auto development, the Autoware Foundation decided to create a new architecture called Autoware Core/Universe.
Autoware Core carries over the original policy of Autoware.Auto to be a stable and well-tested codebase. Alongside Autoware Core is a new concept called Autoware Universe, which acts as an extension of Autoware Core with the following benefits:
This way, the primary requirement of having a stable and safe autonomous driving system can be achieved, whilst simultaneously enabling access to state-of-the-art features created by third-party contributors. For more details about the design of Autoware Core/Universe, refer to the Autoware concepts documentation page.
"},{"location":"design/autoware-interfaces/","title":"Autoware interface design","text":""},{"location":"design/autoware-interfaces/#autoware-interface-design","title":"Autoware interface design","text":""},{"location":"design/autoware-interfaces/#abstract","title":"Abstract","text":"Autoware defines three categories of interfaces. The first one is Autoware AD API for operating the vehicle from outside the autonomous driving system such as the Fleet Management System (FMS) and Human Machine Interface (HMI) for operators or passengers. The second one is Autoware component interface for components to communicate with each other. The last one is the local interface used inside the component.
"},{"location":"design/autoware-interfaces/#concept","title":"Concept","text":"Applications can operate multiple and various vehicles in a common way.
Applications are not affected by version updates and implementation changes.
Developers only need to know the interface to add new features and hardware.
Goals:
Non-goals:
The components of Autoware are connected via the component interface. Each component uses the interface to provide functionality and to access other components. AD API implementation is also a component. Since the functional elements required for AD API are defined as the component interface, other components do not need to consider AD API directly. Tools for evaluation and debugging, such as simulators, access both AD API and the component interface.
The component interface has a hierarchical specification. The top-level architecture consists of some components. Each component has some options of the next-level architecture. Developers select one of them when implementing the component. The simplest next-level architecture is monolithic. This is an all-in-one and black box implementation, and is suitable for small group development, prototyping, and very complex functions. Others are arbitrary architecture consists of sub-components and have advantages for large group development. A sub-component can be combined with others that adopt the same architecture. Third parties can define and publish their own architecture and interface for open source development. It is desirable to propose them for standardization if they are sufficiently evaluated.
"},{"location":"design/autoware-interfaces/#features","title":"Features","text":""},{"location":"design/autoware-interfaces/#communication-methods","title":"Communication methods","text":"As shown in the table below, interfaces are classified into four communication methods to define their behavior. Function Call is a request-response communication and is used for processing that requires immediate results. The others are publish-subscribe communication. Notification is used to process data that changes with some event, typically a callback. Streams handle continuously changing data. Reliable Stream expects all data to arrive without loss, Realtime Stream expects the latest data to arrive with low delay.
Communication Method ROS Implementation Optional Implementation Function Call Service HTTP Notification Topic (reliable, transient_local) MQTT (QoS=2, retain) Reliable Stream Topic (reliable, volatile) MQTT (QoS=2) Realtime Stream Topic (best_effort, volatile) MQTT (QoS=0)These methods are provided as services or topics of ROS since Autoware is developed using ROS and mainly communicates with its packages. On the other hand, FMS and HMI are often implemented without ROS, Autoware is also expected to communicate with applications that do not use ROS. It is wasteful for each of these applications to have an adapter for Autoware, and a more suitable means of communication is required. HTTP and MQTT are suggested as additional options because these protocols are widely used and can substitute the behavior of services and topics. In that case, text formats such as JSON where field names are repeated in an array of objects, are inefficient and it is necessary to consider the serialization.
"},{"location":"design/autoware-interfaces/#naming-convention","title":"Naming convention","text":"The name of the interface must be /<component name>/api/<interface name>, where <component name> is the name of the component. For an AD API component, omit this part and start with /api. The <interface name> is an arbitrary string separated by slashes. Note that this rule causes a restriction that the namespace api must not be used as a name other than AD API and the component interface.
The following are examples of correct interface names for AD API and the component interface:
The following are examples of incorrect interface names for AD API and the component interface:
It is recommended to log the interface for analysis of vehicle behavior. If logging is needed, rosbag is available for topics, and use logger in rclcpp or rclpy for services. Typically, create a wrapper for service and client classes that logs when a service is called.
"},{"location":"design/autoware-interfaces/#restrictions","title":"Restrictions","text":"For each API, consider the restrictions such as following and describe them if necessary.
Services:
Topics:
Do not share the types in AD API unless they are obviously the same to avoid changes in one API affecting another. Also, implementation-dependent types, including the component interface, should not be used in AD API for the same reason. Use the type in AD API in implementation, or create the same type and copy the data to convert the type.
"},{"location":"design/autoware-interfaces/#constants-and-enumeration","title":"Constants and enumeration","text":"Since ROS don't support enumeration, use constants instead. The default value of type such as zero and empty string should not be used to detect that a variable is unassigned. Alternatively, assign it a dedicated name to indicate that it is undefined. If one type has multiple enumerations, comment on the correspondence between constants and variables. Do not use enumeration values directly, as assignments are subject to change when the version is updated.
"},{"location":"design/autoware-interfaces/#time-stamp","title":"Time stamp","text":"Clarify what the timestamp indicates. for example, send time, measurement time, update time, etc. Consider having multiple timestamps if necessary. Use std_msgs/msg/Header when using ROS transform. Also consider whether the header is common to all data, independent for each data, or additional timestamp is required.
Currently, there is no required header.
"},{"location":"design/autoware-interfaces/#response-status","title":"Response status","text":"The interfaces whose communication method is Function Call use a common response status to unify the error format. These interfaces should include a variable of ResponseStatus with the name status in the response. See autoware_adapi_v1_msgs/msg/ResponseStatus for details.
"},{"location":"design/autoware-interfaces/#concerns-assumptions-and-limitations","title":"Concerns, assumptions and limitations","text":"Warning
Under Construction
See here for an overview.
"},{"location":"design/autoware-interfaces/ad-api/list/","title":"List of Autoware AD API","text":""},{"location":"design/autoware-interfaces/ad-api/list/#list-of-autoware-ad-api","title":"List of Autoware AD API","text":"This API manages the behavior related to the abnormality of the vehicle. It provides the state of Request to Intervene (RTI), Minimal Risk Maneuver (MRM) and Minimal Risk Condition (MRC). As shown below, Autoware has the gate to switch between the command during normal operation and the command during abnormal operation. For safety, Autoware switches the operation to MRM when an abnormality is detected. Since the required behavior differs depending on the situation, MRM is implemented in various places as a specific mode in a normal module or as an independent module. The fail-safe module selects the behavior of MRM according to the abnormality and switches the gate output to that command.
"},{"location":"design/autoware-interfaces/ad-api/list/api/fail_safe/#states","title":"States","text":"The MRM state indicates whether MRM is operating. This state also provides success or failure. Generally, MRM will switch to another behavior if it fails.
State Description NONE MRM is not operating. OPERATING MRM is operating because an abnormality has been detected. SUCCEEDED MRM succeeded. The vehicle is in a safe condition. FAILED MRM failed. The vehicle is still in an unsafe condition."},{"location":"design/autoware-interfaces/ad-api/list/api/fail_safe/#behavior","title":"Behavior","text":"There is a dependency between MRM behaviors. For example, it switches from a comfortable stop to a emergency stop, but not the other way around. This is service dependent. Autoware supports the following transitions by default.
State Description NONE MRM is not operating or is operating but no special behavior is required. COMFORTABLE_STOP The vehicle will stop quickly with a comfortable deceleration. EMERGENCY_STOP The vehicle will stop immediately with as much deceleration as possible."},{"location":"design/autoware-interfaces/ad-api/list/api/fail_safe/mrm_state/","title":"Mrm state","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/fail_safe/mrm_state/#apifail_safemrm_state","title":"/api/fail_safe/mrm_state","text":"Get the MRM state. For details, see the fail-safe.
"},{"location":"design/autoware-interfaces/ad-api/list/api/fail_safe/mrm_state/#message","title":"Message","text":"Name Type Description state uint16 The state of MRM operation. behavior uint16 The currently selected behavior of MRM."},{"location":"design/autoware-interfaces/ad-api/list/api/interface/","title":"Interface API","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/interface/#interface-api","title":"Interface API","text":"This API provides the interface version of the set of AD APIs. It follows Semantic Versioning in order to provide an intuitive understanding of the changes between versions.
"},{"location":"design/autoware-interfaces/ad-api/list/api/interface/version/","title":"Version","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/interface/version/#apiinterfaceversion","title":"/api/interface/version","text":"Get the interface version. The version follows Semantic Versioning.
"},{"location":"design/autoware-interfaces/ad-api/list/api/interface/version/#request","title":"Request","text":"None
"},{"location":"design/autoware-interfaces/ad-api/list/api/interface/version/#response","title":"Response","text":"Name Type Description major uint16 major version minor uint16 minor version patch uint16 patch version"},{"location":"design/autoware-interfaces/ad-api/list/api/localization/","title":"Localization API","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/localization/#localization-api","title":"Localization API","text":"This API manages the initialization of localization. Autoware requires a global pose as the initial guess for localization.
"},{"location":"design/autoware-interfaces/ad-api/list/api/localization/#states","title":"States","text":"State Description UNINITIALIZED Localization is not initialized. Waiting for a global pose as the initial guess. INITIALIZING Localization is initializing. INITIALIZED Localization is initialized. Initialization can be requested again if necessary."},{"location":"design/autoware-interfaces/ad-api/list/api/localization/initialization_state/","title":"Initialization state","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/localization/initialization_state/#apilocalizationinitialization_state","title":"/api/localization/initialization_state","text":"Get the initialization state of localization. For details, see the localization initialization state.
"},{"location":"design/autoware-interfaces/ad-api/list/api/localization/initialization_state/#message","title":"Message","text":"Name Type Description state uint16 A value of the localization initialization state."},{"location":"design/autoware-interfaces/ad-api/list/api/localization/initialize/","title":"Initialize","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/localization/initialize/#apilocalizationinitialize","title":"/api/localization/initialize","text":"Request to initialize localization. For details, see the pose state.
"},{"location":"design/autoware-interfaces/ad-api/list/api/localization/initialize/#request","title":"Request","text":"Name Type Description pose geometry_msgs/msg/PoseWithCovarianceStamped[<=1] A global pose as the initial guess. If omitted, the GNSS pose will be used."},{"location":"design/autoware-interfaces/ad-api/list/api/localization/initialize/#response","title":"Response","text":"Name Type Description status autoware_adapi_v1_msgs/msg/ResponseStatus response status"},{"location":"design/autoware-interfaces/ad-api/list/api/motion/","title":"Motion API","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/motion/#motion-api","title":"Motion API","text":"This API manages the current behavior of the vehicle. Applications can notify the vehicle behavior to the people around and visualize it for operator and passengers.
"},{"location":"design/autoware-interfaces/ad-api/list/api/motion/#states","title":"States","text":"The motion state manages the stop and start of the vehicle. Once the vehicle has stopped, the state will be STOPPED. After this, when the vehicle tries to start (is still stopped), the state will be STARTING. In this state, calling the start API changes the state to MOVING and the vehicle starts. This mechanism can add processing such as announcements before the vehicle starts. Depending on the configuration, the state may transition directly from STOPPED to MOVING.
State Description STOPPED The vehicle is stopped. STARTING The vehicle is stopped, but is trying to start. MOVING The vehicle is moving. BRAKING (T.B.D.) The vehicle is decelerating strongly."},{"location":"design/autoware-interfaces/ad-api/list/api/motion/accept_start/","title":"Accept start","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/motion/accept_start/#apimotionaccept_start","title":"/api/motion/accept_start","text":"Accept the vehicle to start. This API can be used when the motion state is STARTING.
"},{"location":"design/autoware-interfaces/ad-api/list/api/motion/accept_start/#request","title":"Request","text":"None
"},{"location":"design/autoware-interfaces/ad-api/list/api/motion/accept_start/#response","title":"Response","text":"Name Type Description status autoware_adapi_v1_msgs/msg/ResponseStatus response status"},{"location":"design/autoware-interfaces/ad-api/list/api/motion/state/","title":"State","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/motion/state/#apimotionstate","title":"/api/motion/state","text":"Get the motion state. For details, see the motion state.
"},{"location":"design/autoware-interfaces/ad-api/list/api/motion/state/#message","title":"Message","text":"Name Type Description state uint16 A value of the motion state."},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/","title":"Operation Mode API","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/#operation-mode-api","title":"Operation Mode API","text":"As shown below, Autoware assumes that the vehicle interface has two modes, Autoware control and direct control. In direct control mode, the vehicle is operated using devices such as steering and pedals. If the vehicle does not support direct control mode, it is always treated as Autoware control mode. Autoware control mode has four operation modes.
Mode Description Stop Keep the vehicle stopped. Autonomous Autonomously control the vehicle. Local Manually control the vehicle from nearby with some device such as a joystick. Remote Manually control the vehicle from a web application on the cloud. "},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/#states","title":"States","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/#autoware-control-flag","title":"Autoware control flag","text":"The flag is_autoware_control_enabled indicates if the vehicle is controlled by Autoware. The enable and disable APIs can be used if the control can be switched by software. These APIs will always fail if the vehicle does not support mode switching or is switched by hardware.
The state operation_mode indicates what command is used when Autoware control is enabled. The flags change_to_* can be used to check if it is possible to transition to each mode.
Since Autoware may not be able to guarantee safety, such as switching to autonomous mode during overspeed. There is the flag is_in_transition for this situation and it will be true when changing modes. The operator who changed the mode should ensure safety while this flag is true. The flag will be false when the mode change is complete.
Change the operation mode to autonomous. For details, see the operation mode.
"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/change_to_autonomous/#request","title":"Request","text":"None
"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/change_to_autonomous/#response","title":"Response","text":"Name Type Description status autoware_adapi_v1_msgs/msg/ResponseStatus response status"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/change_to_local/","title":"Change to local","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/change_to_local/#apioperation_modechange_to_local","title":"/api/operation_mode/change_to_local","text":"Change the operation mode to local. For details, see the operation mode.
"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/change_to_local/#request","title":"Request","text":"None
"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/change_to_local/#response","title":"Response","text":"Name Type Description status autoware_adapi_v1_msgs/msg/ResponseStatus response status"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/change_to_remote/","title":"Change to remote","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/change_to_remote/#apioperation_modechange_to_remote","title":"/api/operation_mode/change_to_remote","text":"Change the operation mode to remote. For details, see the operation mode.
"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/change_to_remote/#request","title":"Request","text":"None
"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/change_to_remote/#response","title":"Response","text":"Name Type Description status autoware_adapi_v1_msgs/msg/ResponseStatus response status"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/change_to_stop/","title":"Change to stop","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/change_to_stop/#apioperation_modechange_to_stop","title":"/api/operation_mode/change_to_stop","text":"Change the operation mode to stop. For details, see the operation mode.
"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/change_to_stop/#request","title":"Request","text":"None
"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/change_to_stop/#response","title":"Response","text":"Name Type Description status autoware_adapi_v1_msgs/msg/ResponseStatus response status"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/disable_autoware_control/","title":"Disable autoware control","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/disable_autoware_control/#apioperation_modedisable_autoware_control","title":"/api/operation_mode/disable_autoware_control","text":"Disable vehicle control by Autoware. For details, see the operation mode. This API fails if the vehicle does not support mode change by software.
"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/disable_autoware_control/#request","title":"Request","text":"None
"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/disable_autoware_control/#response","title":"Response","text":"Name Type Description status autoware_adapi_v1_msgs/msg/ResponseStatus response status"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/enable_autoware_control/","title":"Enable autoware control","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/enable_autoware_control/#apioperation_modeenable_autoware_control","title":"/api/operation_mode/enable_autoware_control","text":"Enable vehicle control by Autoware. For details, see the operation mode. This API fails if the vehicle does not support mode change by software.
"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/enable_autoware_control/#request","title":"Request","text":"None
"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/enable_autoware_control/#response","title":"Response","text":"Name Type Description status autoware_adapi_v1_msgs/msg/ResponseStatus response status"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/state/","title":"State","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/state/#apioperation_modestate","title":"/api/operation_mode/state","text":"Get the operation mode state. For details, see the operation mode.
"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/state/#message","title":"Message","text":"Name Type Description mode uint8 The selected command for Autoware control. is_autoware_control_enabled bool True if vehicle control by Autoware is enabled. is_in_transition bool True if the operation mode is in transition. is_stop_mode_available bool True if the operation mode can be changed to stop. is_autonomous_mode_available bool True if the operation mode can be changed to autonomous. is_local_mode_available bool True if the operation mode can be changed to local. is_remote_mode_available bool True if the operation mode can be changed to remote."},{"location":"design/autoware-interfaces/ad-api/list/api/planning/","title":"Planning API","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/planning/#planning-api","title":"Planning API","text":"This API manages the planned behavior of the vehicle. Applications can notify the vehicle behavior to the people around and visualize it for operator and passengers.
"},{"location":"design/autoware-interfaces/ad-api/list/api/planning/#velocity-factors","title":"Velocity factors","text":"The velocity factors is an array of information on the behavior that the vehicle stops (or slows down). Each factor has a type shown below, pose in the base link, distance, status, and detailed data depending on its type. As the vehicle approaches the stop position, this factor appears with a status of APPROACHING. And when the vehicle reaches that position and stops, the status will be STOPPED. The pose indicates the stop position or the base link if the stop position cannot be calculated.
Factor Type Description SURROUNDING_OBSTACLE There are obstacles immediately around the vehicle. ROUTE_OBSTACLE There are obstacles along the route ahead. INTERSECTION There are obstacles in other lanes in the path. CROSSWALK There are obstacles on the crosswalk. REAR_CHECK There are obstacles behind that would be in a human driver's blind spot. USER_DEFINED_DETECTION_AREA There are obstacles in the predefined detection area. NO_STOPPING_AREA There is not enough space beyond the no stopping area. STOP_SIGN A stop by a stop sign. TRAFFIC_SIGNAL A stop by a traffic signal. V2I_GATE_CONTROL_ENTER A stop by a V2I gate entering. V2I_GATE_CONTROL_LEAVE A stop by a V2I gate leaving. MERGE A stop before merging lanes. SIDEWALK A stop before crossing the sidewalk. LANE_CHANGE A lane change. AVOIDANCE A path change to avoid an obstacle in the current lane. EMERGENCY_OPERATION A stop by emergency instruction from the operator."},{"location":"design/autoware-interfaces/ad-api/list/api/planning/#steering-factors","title":"Steering factors","text":"The steering factors is an array of information on the maneuver that requires use of turn indicators, such as turning left or right. Each factor has a type shown below, pose in the base link, distance, status, and detailed data depending on its type. As the vehicle approaches the position to start steering, this factor appears with a status of APPROACHING. And when the vehicle reaches that position, the status will be TURNING. The pose indicates the start position when APPROACHING and the end position when TURNING.
In cases such as lane change and avoidance, the vehicle will start steering at any position in the range depending on the situation. As the vehicle approaches the start position of the range, this factor appears with a status of APPROACHING. And when the vehicle reaches that position, the status will be TRYING. Then, when it is possible, the vehicle will start steering and the status will be TURNING. The pose indicates the start of the range (A) when APPROACHING and the end of the range (B) when TRYING. The position to end steering (C to D) for TURNING depends on the position to start steering.
Factor Type Description INTERSECTION A turning left or right at an intersection. LANE_CHANGE A lane change. AVOIDANCE_PATH_CHANGE A path change to avoid an obstacle in the current lane. AVOIDANCE_PATH_RETURN A path change to return to the original lane after avoiding an obstacle. STATION T.B.D. (bus stop) PULL_OUT T.B.D. PULL_OVER T.B.D. EMERGENCY_OPERATION A path change by emergency instruction from the operator."},{"location":"design/autoware-interfaces/ad-api/list/api/planning/steering_factors/","title":"Steering factors","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/planning/steering_factors/#apiplanningsteering_factors","title":"/api/planning/steering_factors","text":"Get the steering factors, sorted in ascending order of distance. For details, see the planning.
"},{"location":"design/autoware-interfaces/ad-api/list/api/planning/steering_factors/#message","title":"Message","text":"Name Type Description factors.pose geometry_msgs/msg/Pose[2] The base link pose related to the steering factor. factors.distance float32[2] The distance from the base link to the above pose. factors.type uint16 The type of the steering factor. factors.direction uint16 The direction of the steering factor. factors.status uint16 The status of the steering factor. factors.detail string The additional information of the steering factor."},{"location":"design/autoware-interfaces/ad-api/list/api/planning/velocity_factors/","title":"Velocity factors","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/planning/velocity_factors/#apiplanningvelocity_factors","title":"/api/planning/velocity_factors","text":"Get the velocity factors, sorted in ascending order of distance. For details, see the planning.
"},{"location":"design/autoware-interfaces/ad-api/list/api/planning/velocity_factors/#message","title":"Message","text":"Name Type Description factors.pose geometry_msgs/msg/Pose The base link pose related to the velocity factor. factors.distance float32 The distance from the base link to the above pose. factors.type uint16 The type of the velocity factor. factors.status uint16 The status of the velocity factor. factors.detail string The additional information of the velocity factor."},{"location":"design/autoware-interfaces/ad-api/list/api/routing/","title":"Route API","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/routing/#route-api","title":"Route API","text":"This API manages destination and waypoints. Note that waypoints are not like stops and just points passing through. In other words, Autoware does not support the route with multiple stops, the application needs to split it up and switch them. There are two ways to set the route. The one is a generic method that uses pose, another is a map-dependent.
"},{"location":"design/autoware-interfaces/ad-api/list/api/routing/#states","title":"States","text":"State Description UNSET The route is not set. Waiting for a route request. SET The route is set. ARRIVED The vehicle has arrived at the destination. CHANGING Trying to change the route. Not implemented yet."},{"location":"design/autoware-interfaces/ad-api/list/api/routing/clear_route/","title":"Clear route","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/routing/clear_route/#apiroutingclear_route","title":"/api/routing/clear_route","text":"Clear the route.
"},{"location":"design/autoware-interfaces/ad-api/list/api/routing/clear_route/#request","title":"Request","text":"None
"},{"location":"design/autoware-interfaces/ad-api/list/api/routing/clear_route/#response","title":"Response","text":"Name Type Description status autoware_adapi_v1_msgs/msg/ResponseStatus response status"},{"location":"design/autoware-interfaces/ad-api/list/api/routing/route/","title":"Route","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/routing/route/#apiroutingroute","title":"/api/routing/route","text":"Get the route with the waypoint segments in lanelet format. It is empty if route is not set.
"},{"location":"design/autoware-interfaces/ad-api/list/api/routing/route/#message","title":"Message","text":"Name Type Description header std_msgs/msg/Header header for pose transformation data autoware_adapi_v1_msgs/msg/RouteData[<=1] The route in lanelet format"},{"location":"design/autoware-interfaces/ad-api/list/api/routing/set_route/","title":"Set route","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/routing/set_route/#apiroutingset_route","title":"/api/routing/set_route","text":"Set the route with the waypoint segments in lanelet format. If start pose is not specified, the current pose will be used.
"},{"location":"design/autoware-interfaces/ad-api/list/api/routing/set_route/#request","title":"Request","text":"Name Type Description header std_msgs/msg/Header header for pose transformation goal geometry_msgs/msg/Pose goal pose segments autoware_adapi_v1_msgs/msg/RouteSegment[] waypoint segments in lanelet format"},{"location":"design/autoware-interfaces/ad-api/list/api/routing/set_route/#response","title":"Response","text":"Name Type Description status autoware_adapi_v1_msgs/msg/ResponseStatus response status"},{"location":"design/autoware-interfaces/ad-api/list/api/routing/set_route_points/","title":"Set route points","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/routing/set_route_points/#apiroutingset_route_points","title":"/api/routing/set_route_points","text":"Set the route with the waypoint poses. If start pose is not specified, the current pose will be used.
"},{"location":"design/autoware-interfaces/ad-api/list/api/routing/set_route_points/#request","title":"Request","text":"Name Type Description header std_msgs/msg/Header header for pose transformation goal geometry_msgs/msg/Pose goal pose waypoints geometry_msgs/msg/Pose[] waypoint poses"},{"location":"design/autoware-interfaces/ad-api/list/api/routing/set_route_points/#response","title":"Response","text":"Name Type Description status autoware_adapi_v1_msgs/msg/ResponseStatus response status"},{"location":"design/autoware-interfaces/ad-api/list/api/routing/state/","title":"State","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/routing/state/#apiroutingstate","title":"/api/routing/state","text":"Get the route state. For details, see the route state.
"},{"location":"design/autoware-interfaces/ad-api/list/api/routing/state/#message","title":"Message","text":"Name Type Description state uint16 A value of the route state."},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/localization_initialization_state/","title":"Localization initialization state","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/localization_initialization_state/#autoware_adapi_v1_msgsmsglocalizationinitializationstate","title":"autoware_adapi_v1_msgs/msg/LocalizationInitializationState","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/localization_initialization_state/#definition","title":"Definition","text":"uint16 UNKNOWN = 0\nuint16 UNINITIALIZED = 1\nuint16 INITIALIZING = 2\nuint16 INITIALIZED = 3\n\nbuiltin_interfaces/Time stamp\nuint16 state\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/localization_initialization_state/#this-type-is-used-by","title":"This type is used by","text":"None
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/motion_state/","title":"Motion state","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/motion_state/#autoware_adapi_v1_msgsmsgmotionstate","title":"autoware_adapi_v1_msgs/msg/MotionState","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/motion_state/#definition","title":"Definition","text":"uint16 UNKNOWN = 0\nuint16 STOPPED = 1\nuint16 STARTING = 2\nuint16 MOVING = 3\n\nbuiltin_interfaces/Time stamp\nuint16 state\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/motion_state/#this-type-is-used-by","title":"This type is used by","text":"None
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/mrm_state/","title":"Mrm state","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/mrm_state/#autoware_adapi_v1_msgsmsgmrmstate","title":"autoware_adapi_v1_msgs/msg/MrmState","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/mrm_state/#definition","title":"Definition","text":"builtin_interfaces/Time stamp\n\n# For common use\nuint16 UNKNOWN = 0\n\n# For state\nuint16 NORMAL = 1\nuint16 MRM_OPERATING = 2\nuint16 MRM_SUCCEEDED = 3\nuint16 MRM_FAILED = 4\n\n# For behavior\nuint16 NONE = 1\nuint16 EMERGENCY_STOP = 2\nuint16 COMFORTABLE_STOP = 3\n\nuint16 state\nuint16 behavior\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/mrm_state/#this-type-is-used-by","title":"This type is used by","text":"None
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/operation_mode_state/","title":"Operation mode state","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/operation_mode_state/#autoware_adapi_v1_msgsmsgoperationmodestate","title":"autoware_adapi_v1_msgs/msg/OperationModeState","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/operation_mode_state/#definition","title":"Definition","text":"# constants for mode\nuint8 UNKNOWN = 0\nuint8 STOP = 1\nuint8 AUTONOMOUS = 2\nuint8 LOCAL = 3\nuint8 REMOTE = 4\n\n# variables\nbuiltin_interfaces/Time stamp\nuint8 mode\nbool is_autoware_control_enabled\nbool is_in_transition\nbool is_stop_mode_available\nbool is_autonomous_mode_available\nbool is_local_mode_available\nbool is_remote_mode_available\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/operation_mode_state/#this-type-is-used-by","title":"This type is used by","text":"None
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/response_status/","title":"Response status","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/response_status/#autoware_adapi_v1_msgsmsgresponsestatus","title":"autoware_adapi_v1_msgs/msg/ResponseStatus","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/response_status/#definition","title":"Definition","text":"# error code\nuint16 UNKNOWN = 50000\nuint16 SERVICE_UNREADY = 50001\nuint16 SERVICE_TIMEOUT = 50002\nuint16 TRANSFORM_ERROR = 50003\nuint16 PARAMETER_ERROR = 50004\n\n# warning code\nuint16 DEPRECATED = 60000\nuint16 NO_EFFECT = 60001\n\n# variables\nbool success\nuint16 code\nstring message\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/response_status/#this-type-is-used-by","title":"This type is used by","text":"std_msgs/Header header\nautoware_adapi_v1_msgs/RouteData[<=1] data\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/route_data/","title":"Route data","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/route_data/#autoware_adapi_v1_msgsmsgroutedata","title":"autoware_adapi_v1_msgs/msg/RouteData","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/route_data/#definition","title":"Definition","text":"geometry_msgs/Pose start\ngeometry_msgs/Pose goal\nautoware_adapi_v1_msgs/RouteSegment[] segments\nint64 id\nstring type # The same id may be used for each type.\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/route_primitive/#this-type-is-used-by","title":"This type is used by","text":"autoware_adapi_v1_msgs/RoutePrimitive preferred\nautoware_adapi_v1_msgs/RoutePrimitive[] alternatives # Does not include the preferred primitive.\nuint16 UNKNOWN = 0\nuint16 UNSET = 1\nuint16 SET = 2\nuint16 ARRIVED = 3\nuint16 CHANGING = 4\n\nbuiltin_interfaces/Time stamp\nuint16 state\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/route_state/#this-type-is-used-by","title":"This type is used by","text":"None
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/steering_factor/","title":"Steering factor","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/steering_factor/#autoware_adapi_v1_msgsmsgsteeringfactor","title":"autoware_adapi_v1_msgs/msg/SteeringFactor","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/steering_factor/#definition","title":"Definition","text":"# constants for common use\nuint16 UNKNOWN = 0\n\n# constants for type\nuint16 INTERSECTION = 1\nuint16 LANE_CHANGE = 2\nuint16 AVOIDANCE_PATH_CHANGE = 3\nuint16 AVOIDANCE_PATH_RETURN = 4\nuint16 STATION = 5\nuint16 PULL_OUT = 6\nuint16 PULL_OVER = 7\nuint16 EMERGENCY_OPERATION = 8\n\n# constants for direction\nuint16 LEFT = 1\nuint16 RIGHT = 2\nuint16 STRAIGHT = 3\n\n# constants for status\nuint16 APPROACHING = 1\nuint16 TRYING = 2\nuint16 TURNING = 3\n\n# variables\ngeometry_msgs/Pose[2] pose\nfloat32[2] distance\nuint16 type\nuint16 direction\nuint16 status\nstring detail\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/steering_factor/#this-type-is-used-by","title":"This type is used by","text":"std_msgs/Header header\nautoware_adapi_v1_msgs/SteeringFactor[] factors\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/velocity_factor/","title":"Velocity factor","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/velocity_factor/#autoware_adapi_v1_msgsmsgvelocityfactor","title":"autoware_adapi_v1_msgs/msg/VelocityFactor","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/velocity_factor/#definition","title":"Definition","text":"# constants for common use\nuint16 UNKNOWN = 0\n\n# constants for type\nuint16 SURROUNDING_OBSTACLE = 1\nuint16 ROUTE_OBSTACLE = 2\nuint16 INTERSECTION = 3\nuint16 CROSSWALK = 4\nuint16 REAR_CHECK = 5\nuint16 USER_DEFINED_DETECTION_AREA = 6\nuint16 NO_STOPPING_AREA = 7\nuint16 STOP_SIGN = 8\nuint16 TRAFFIC_SIGNAL = 9\nuint16 V2I_GATE_CONTROL_ENTER = 10\nuint16 V2I_GATE_CONTROL_LEAVE = 11\nuint16 MERGE = 12\nuint16 SIDEWALK = 13\nuint16 LANE_CHANGE = 14\nuint16 AVOIDANCE = 15\nuint16 EMERGENCY_STOP_OPERATION = 16\n\n# constants for status\nuint16 APPROACHING = 1\nuint16 STOPPED = 2\n\n# variables\ngeometry_msgs/Pose pose\nfloat32 distance\nuint16 type\nuint16 status\nstring detail\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/velocity_factor/#this-type-is-used-by","title":"This type is used by","text":"std_msgs/Header header\nautoware_adapi_v1_msgs/VelocityFactor[] factors\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/accept_start/","title":"Accept start","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/accept_start/#autoware_adapi_v1_msgssrvacceptstart","title":"autoware_adapi_v1_msgs/srv/AcceptStart","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/accept_start/#definition","title":"Definition","text":"---\nuint16 ERROR_NOT_STARTING = 1\nautoware_adapi_v1_msgs/ResponseStatus status\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/change_operation_mode/","title":"Change operation mode","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/change_operation_mode/#autoware_adapi_v1_msgssrvchangeoperationmode","title":"autoware_adapi_v1_msgs/srv/ChangeOperationMode","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/change_operation_mode/#definition","title":"Definition","text":"---\nuint16 ERROR_NOT_AVAILABLE = 1\nuint16 ERROR_IN_TRANSITION = 2\nautoware_adapi_v1_msgs/ResponseStatus status\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/clear_route/","title":"Clear route","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/clear_route/#autoware_adapi_v1_msgssrvclearroute","title":"autoware_adapi_v1_msgs/srv/ClearRoute","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/clear_route/#definition","title":"Definition","text":"---\nautoware_adapi_v1_msgs/ResponseStatus status\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/initialize_localization/","title":"Initialize localization","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/initialize_localization/#autoware_adapi_v1_msgssrvinitializelocalization","title":"autoware_adapi_v1_msgs/srv/InitializeLocalization","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/initialize_localization/#definition","title":"Definition","text":"geometry_msgs/PoseWithCovarianceStamped[<=1] pose\n---\nuint16 ERROR_UNSAFE = 1\nuint16 ERROR_GNSS_SUPPORT = 2\nuint16 ERROR_GNSS = 3\nuint16 ERROR_ESTIMATION = 4\nautoware_adapi_v1_msgs/ResponseStatus status\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/set_route/","title":"Set route","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/set_route/#autoware_adapi_v1_msgssrvsetroute","title":"autoware_adapi_v1_msgs/srv/SetRoute","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/set_route/#definition","title":"Definition","text":"std_msgs/Header header\ngeometry_msgs/Pose goal\nautoware_adapi_v1_msgs/RouteSegment[] segments\n---\nuint16 ERROR_ROUTE_EXISTS = 1\nuint16 ERROR_PLANNER_UNREADY = 2\nuint16 ERROR_PLANNER_FAILED = 3\nautoware_adapi_v1_msgs/ResponseStatus status\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/set_route_points/","title":"Set route points","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/set_route_points/#autoware_adapi_v1_msgssrvsetroutepoints","title":"autoware_adapi_v1_msgs/srv/SetRoutePoints","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/set_route_points/#definition","title":"Definition","text":"std_msgs/Header header\ngeometry_msgs/Pose goal\ngeometry_msgs/Pose[] waypoints\n---\nuint16 ERROR_ROUTE_EXISTS = 1\nuint16 ERROR_PLANNER_UNREADY = 2\nuint16 ERROR_PLANNER_FAILED = 3\nautoware_adapi_v1_msgs/ResponseStatus status\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_version_msgs/srv/interface_version/","title":"Interface version","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_version_msgs/srv/interface_version/#autoware_adapi_version_msgssrvinterfaceversion","title":"autoware_adapi_version_msgs/srv/InterfaceVersion","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_version_msgs/srv/interface_version/#definition","title":"Definition","text":"---\nuint16 major\nuint16 minor\nuint16 patch\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_version_msgs/srv/interface_version/#this-type-is-used-by","title":"This type is used by","text":"None
"},{"location":"design/autoware-interfaces/ad-api/use-cases/","title":"Use cases of Autoware AD API","text":""},{"location":"design/autoware-interfaces/ad-api/use-cases/#use-cases-of-autoware-ad-api","title":"Use cases of Autoware AD API","text":""},{"location":"design/autoware-interfaces/ad-api/use-cases/#user-stories","title":"User stories","text":"The user stories are service scenarios that AD API assumes. AD API is designed based on these scenarios. Each scenario is realized by a combination of use cases described later. If there are scenarios that cannot be covered, please discuss adding a user story.
Use cases are partial scenarios derived from the user story and generically designed. Service providers can combine these use cases to define user stories and check if AD API can be applied to their own scenarios.
This user story is a bus service that goes around the designated stops.
"},{"location":"design/autoware-interfaces/ad-api/use-cases/bus-service/#scenario","title":"Scenario","text":"Step Operation Use Case 1 Startup the autonomous driving system. Launch and terminate 2 Drive the vehicle from the garage to the waiting position. Change the operation mode 3 Enable autonomous control. Change the operation mode 4 Drive the vehicle to the next bus stop. Drive to the designated position 5 Get on and off the vehicle. Get on and get off 6 Return to step 4 unless it's the last bus stop. 7 Drive the vehicle to the waiting position. Drive to the designated position 8 Drive the vehicle from the waiting position to the garage. Change the operation mode 9 Shutdown the autonomous driving system. Launch and terminate"},{"location":"design/autoware-interfaces/ad-api/use-cases/change-operation-mode/","title":"Change the operation mode","text":""},{"location":"design/autoware-interfaces/ad-api/use-cases/change-operation-mode/#change-the-operation-mode","title":"Change the operation mode","text":""},{"location":"design/autoware-interfaces/ad-api/use-cases/change-operation-mode/#related-api","title":"Related API","text":"Change the mode with software switch.
Change the mode with hardware switch.
Initialization of the pose using input.
Initialization of the pose using GNSS.
This user story is a taxi service that picks up passengers and drives them to their destination.
"},{"location":"design/autoware-interfaces/ad-api/use-cases/taxi-service/#scenario","title":"Scenario","text":"Step Operation Use Case 1 Startup the autonomous driving system. Launch and terminate 2 Drive the vehicle from the garage to the waiting position. Change the operation mode 3 Enable autonomous control. Change the operation mode 4 Drive the vehicle to the position to pick up. Drive to the designated position 5 Get on the vehicle. Get on and get off 6 Drive the vehicle to the destination. Drive to the designated position 7 Get off the vehicle. Get on and get off 8 Drive the vehicle to the waiting position. Drive to the designated position 9 Return to step 4 if there is another request. 10 Drive the vehicle from the waiting position to the garage. Change the operation mode 11 Shutdown the autonomous driving system. Launch and terminate"},{"location":"design/autoware-interfaces/components/","title":"Component interfaces","text":""},{"location":"design/autoware-interfaces/components/#component-interfaces","title":"Component interfaces","text":"Warning
Under Construction
See here for an overview.
"},{"location":"design/autoware-interfaces/components/control/","title":"Control","text":""},{"location":"design/autoware-interfaces/components/control/#control","title":"Control","text":""},{"location":"design/autoware-interfaces/components/control/#inputs","title":"Inputs","text":""},{"location":"design/autoware-interfaces/components/control/#vehicle-kinematic-state","title":"Vehicle kinematic state","text":"Current position and orientation of ego. Published by the Localization module.
trajectory to be followed by the controller. See Outputs of Planning.
"},{"location":"design/autoware-interfaces/components/control/#steering-status","title":"Steering Status","text":"Current steering of the ego vehicle. Published by the Vehicle Interface.
Actuation status of the ego vehicle for acceleration, steering, and brake.
TODO This represents the reported physical efforts exerted by the vehicle actuators. Published by the Vehicle Interface.
A motion signal to drive the vehicle, achieved by the low-level controller in the vehicle layer. Used by the Vehicle Interface.
Environment map created with point cloud, published by the map server.
A 3d point cloud map is used for LiDAR-based localization in Autoware.
"},{"location":"design/autoware-interfaces/components/localization/#manual-initial-pose","title":"Manual Initial Pose","text":"Start pose of ego, published by the user interface.
LiDAR scanning for NDT matching, published by the LiDAR sensor.
The raw 3D-LiDAR data needs to be processed by the point cloud pre-processing modules before being used for localization.
"},{"location":"design/autoware-interfaces/components/localization/#automatic-initial-pose","title":"Automatic Initial pose","text":"Start pose of ego, calculated from INS(Inertial navigation sensor) sensing data.
When the initial pose is not set manually, the message can be used for automatic pose initialization.
Current Geographic coordinate of the ego, published by the GNSS sensor.
Current orientation of the ego, published by the GNSS-INS.
Current orientation, angular velocity and linear acceleration of ego, calculated from IMU sensing data.
Current velocity of the ego vehicle, published by the vehicle interface.
Before the velocity input localization interface, module vehicle_velocity_converter converts message type autoware_auto_vehicle_msgs/msg/VelocityReport to geometry_msgs/msg/TwistWithCovarianceStamped.
Current pose of ego, calculated from localization interface.
Current velocity of ego, calculated from localization interface.
Current acceleration of ego, calculated from localization interface.
Current pose, velocity and acceleration of ego, calculated from localization interface.
Note: Kinematic state contains pose, velocity and acceleration. In the future, pose, velocity and acceleration will not be used as output for localization.
The message will be subscribed by the planning and control module.
"},{"location":"design/autoware-interfaces/components/localization/#localization-accuracy","title":"Localization Accuracy","text":"Diagnostics information that indicates if the localization module works properly.
TBD.
"},{"location":"design/autoware-interfaces/components/map/","title":"Map","text":""},{"location":"design/autoware-interfaces/components/map/#map","title":"Map","text":""},{"location":"design/autoware-interfaces/components/map/#overview","title":"Overview","text":"Autoware relies on high-definition point cloud maps and vector maps of the driving environment to perform various tasks. Before launching Autoware, you need to load the pre-created map files.
"},{"location":"design/autoware-interfaces/components/map/#inputs","title":"Inputs","text":".pcd).osm)Refer to Creating maps on how to create maps.
"},{"location":"design/autoware-interfaces/components/map/#outputs","title":"Outputs","text":""},{"location":"design/autoware-interfaces/components/map/#point-cloud-map","title":"Point cloud map","text":"It loads point cloud files and publishes the maps to the other Autoware nodes in various configurations. Currently, it supports the following types:
It loads a Lanelet2 file and publishes the map data as autoware_auto_mapping_msgs/msg/HADMapBin message. The lan/lon coordinates are projected onto the MGRS coordinates.
Visualize autoware_auto_mapping_msgs/HADMapBin messages in Rviz.
Warning
Under Construction
This page provides specific specifications about the Interface of the Perception Component. Please refer to the perception architecture reference implementation design document for concepts and data flow.
"},{"location":"design/autoware-interfaces/components/perception/#input","title":"Input","text":""},{"location":"design/autoware-interfaces/components/perception/#from-map-component","title":"From Map Component","text":"Name Topic / Service Type Description Vector Map/map/vector_map autoware_auto_mapping_msgs/msg/HADMapBin HD Map including the information about lanes Point Cloud Map /service/get_differential_pcd_map autoware_map_msgs/srv/GetDifferentialPointCloudMap Point Cloud Map Notes:
/sensing/camera/camera*/image_rect_color sensor_msgs/Image Camera image data, processed with Lens Distortion Correction (LDC) Camera Image /sensing/camera/camera*/image_raw sensor_msgs/Image Camera image data, not processed with Lens Distortion Correction (LDC) Point Cloud /sensing/lidar/concatenated/pointcloud sensor_msgs/PointCloud2 Concatenated point cloud from multiple LiDAR sources Radar Object /sensing/radar/detected_objects autoware_auto_perception_msgs/msg/DetectedObject Radar objects"},{"location":"design/autoware-interfaces/components/perception/#from-localization-component","title":"From Localization Component","text":"Name Topic Type Description Vehicle Odometry /localization/kinematic_state nav_msgs/msg/Odometry Ego vehicle odometry topic"},{"location":"design/autoware-interfaces/components/perception/#from-api","title":"From API","text":"Name Topic Type Description External Traffic Signals /external/traffic_signals autoware_perception_msgs::msg::TrafficSignalArray The traffic signals from an external system"},{"location":"design/autoware-interfaces/components/perception/#output","title":"Output","text":""},{"location":"design/autoware-interfaces/components/perception/#to-planning","title":"To Planning","text":"Name Topic Type Description Dynamic Objects /perception/object_recognition/objects autoware_auto_perception_msgs/msg/PredictedObjects Set of dynamic objects with information such as a object class and a shape of the objects Obstacles /perception/obstacle_segmentation/pointcloud sensor_msgs/PointCloud2 Obstacles, which includes dynamic objects and static objetcs Occupancy Grid Map /perception/occupancy_grid_map/map nav_msgs/msg/OccupancyGrid The map with the imformation about the presence of obstacles and blind spot Traffic Signal /perception/traffic_light_recognition/traffic_signals autoware_perception_msgs::msg::TrafficSignalArray The traffic signal information such as a color (green, yellow, read) and an arrow (right, left, straight)"},{"location":"design/autoware-interfaces/components/planning/","title":"Planning","text":""},{"location":"design/autoware-interfaces/components/planning/#planning","title":"Planning","text":""},{"location":"design/autoware-interfaces/components/planning/#inputs","title":"Inputs","text":""},{"location":"design/autoware-interfaces/components/planning/#3d-object-predictions","title":"3D Object Predictions","text":"set of perceived objects around ego that need to be avoided when planning a trajectory. Published by the Perception module.
Service response with traffic light information. The message definition is under discussion.
With the traffic_light_state being one of the following
current position and orientation of ego. Published by the Localization module.
map of the environment where the planning takes place. Published by the Map Server.
target pose of ego. Published by the User Interface.
TBD.
The message definition is under discussion.
"},{"location":"design/autoware-interfaces/components/planning/#error-status","title":"Error status","text":"a status corresponding to the current state of Autoware. Used by the Vehicle Interface to switch between different modes in case of emergency. Published by the Diagnostic Manager.
With the state being one of the following:
[TODO] original design for these messages: diagnostic manager also publishes an overriding emergency control command (Add the monitoring system related messages - Autoware.Auto). Possible new design: gate of the vehicle interface switches to the emergency control command (generated by another controller) when receiving an OVERRIDE_REQUESTING message.
The message definition is under discussion.
"},{"location":"design/autoware-interfaces/components/planning/#outputs","title":"Outputs","text":""},{"location":"design/autoware-interfaces/components/planning/#traffic-light-query","title":"Traffic Light Query","text":"service request for the state of a specific traffic light. Sent to the Perception module.
The message definition is under discussion.
"},{"location":"design/autoware-interfaces/components/planning/#trajectory","title":"Trajectory","text":"A sequence of space and velocity points to be followed by the controller.
Commands for various elements of the vehicle unrelated to motion. Sent to the Vehicle Interface. (For the definition, see autoware_auto_vehicle_msgs.)
TBD.
The message definition is under discussion.
"},{"location":"design/autoware-interfaces/components/planning/#engagement-request","title":"Engagement Request","text":"TBD,
The message definition is under discussion.
"},{"location":"design/autoware-interfaces/components/vehicle-dimensions/","title":"Vehicle dimensions","text":""},{"location":"design/autoware-interfaces/components/vehicle-dimensions/#vehicle-dimensions","title":"Vehicle dimensions","text":""},{"location":"design/autoware-interfaces/components/vehicle-dimensions/#vehicle-axes-and-base_link","title":"Vehicle axes and base_link","text":"The base_link frame is used very frequently throughout the Autoware stack, and is a projection of the rear-axle center onto the ground surface.
map to base_link transformation.base_link frame should be in the future.base_link to incoming poses.The distance between front and rear axles.
"},{"location":"design/autoware-interfaces/components/vehicle-dimensions/#track_width","title":"track_width","text":"The distance between left and right wheels.
"},{"location":"design/autoware-interfaces/components/vehicle-dimensions/#overhangs","title":"Overhangs","text":"Overhangs are part of the minimum safety box calculation.
When measuring overhangs, side mirrors, protruding sensors and wheels should be taken into consideration.
"},{"location":"design/autoware-interfaces/components/vehicle-dimensions/#left_overhang","title":"left_overhang","text":"The distance between the axis centers of the left wheels and the left-most point of the vehicle.
"},{"location":"design/autoware-interfaces/components/vehicle-dimensions/#right_overhang","title":"right_overhang","text":"The distance between the axis centers of the right wheels and the right-most point of the vehicle.
"},{"location":"design/autoware-interfaces/components/vehicle-dimensions/#front_overhang","title":"front_overhang","text":"The distance between the front axle and the foremost point of the vehicle.
"},{"location":"design/autoware-interfaces/components/vehicle-dimensions/#rear_overhang","title":"rear_overhang","text":"The distance between the rear axle and the rear-most point of the vehicle.
"},{"location":"design/autoware-interfaces/components/vehicle-dimensions/#vehicle_length","title":"vehicle_length","text":"Total length of the vehicle. Calculated by front_overhang + wheelbase + rear_overhang
Total width of the vehicle. Calculated by left_overhang + track_width + right_overhang
The lateral width of a wheel tire, primarily used for dead reckoning.
"},{"location":"design/autoware-interfaces/components/vehicle-dimensions/#wheel_radius","title":"wheel_radius","text":"The radius of the wheel, primarily used for dead reckoning.
"},{"location":"design/autoware-interfaces/components/vehicle-dimensions/#polygon_footprint","title":"polygon_footprint","text":"The polygon defines the minimum collision area for the vehicle.
The points should be ordered clockwise, with the origin on the base_link.
If the vehicle is going forward, a positive wheel angle will result in the vehicle turning left.
Autoware assumes the rear wheels don't turn on z axis.
The vehicle used in the illustrations was created by xvlblo22 and is from https://www.turbosquid.com/3d-models/modular-sedan-3d-model-1590886.
"},{"location":"design/autoware-interfaces/components/vehicle-interface/","title":"Vehicle Interface","text":""},{"location":"design/autoware-interfaces/components/vehicle-interface/#vehicle-interface","title":"Vehicle Interface","text":"The Vehicle Interface receives the Vehicle Signal Commands and Vehicle Control Commands and publishes the vehicle status. It also communicates with vehicle by the vehicle-specific protocol.
The Gate switches multiple Vehicle Control Commands. These signals include autonomous diving command, joystick, remote control, and emergency operation, etc. The Adapter converts generalized control command (target steering, steering rate, velocity, acceleration, jerk) into vehicle-specific control values (steering-torque, wheel-torque, voltage, pressure, accel pedal position, etc).
(See Inputs of Planning.)
"},{"location":"design/autoware-interfaces/components/vehicle-interface/#vehicle-control-command","title":"Vehicle Control Command","text":"(See Output of Control.)
"},{"location":"design/autoware-interfaces/components/vehicle-interface/#vehicle-signals-commands","title":"Vehicle Signals Commands","text":"Commands for various elements of the vehicle unrelated to motion. Published by the Planning module.
"},{"location":"design/autoware-interfaces/components/vehicle-interface/#outputs","title":"Outputs","text":""},{"location":"design/autoware-interfaces/components/vehicle-interface/#vehicle-signal-reports","title":"Vehicle Signal Reports","text":"Reports for various elements of the vehicle unrelated to motion. Published by the Vehicle Interface.
"},{"location":"design/autoware-interfaces/components/vehicle-interface/#vehicle-odometry","title":"Vehicle Odometry","text":"Odometry of the vehicle. Used by the Localization module to update the pose of the vehicle in the map.
Steering of the ego vehicle. Published by the Vehicle Interface.
Actuation status of the ego vehicle for acceleration, steering, and brake. This represents the reported physical efforts exerted by the vehicle actuators. Published by the Vehicle Interface.
The message definition is under discussion.
"},{"location":"design/autoware-interfaces/components/vehicle-interface/#actuation-command","title":"Actuation Command","text":"Actuation command sent to the ego vehicle. This represents the requested physical efforts to be exerted by the vehicle actuators. Published by the Vehicle Interface as generated by the adapter.
The message definition is under discussion.
"},{"location":"design/autoware-interfaces/components/vehicle-interface/#vehicle-communication","title":"Vehicle Communication","text":"Vehicle specific messages protocol like CAN (Controller Area Network).
"},{"location":"design/configuration-management/","title":"Configuration management","text":""},{"location":"design/configuration-management/#configuration-management","title":"Configuration management","text":"Warning
Under Construction
"},{"location":"design/configuration-management/development-process/","title":"Development process","text":""},{"location":"design/configuration-management/development-process/#development-process","title":"Development process","text":"Warning
Under Construction
"},{"location":"design/configuration-management/release-process/","title":"Release process","text":""},{"location":"design/configuration-management/release-process/#release-process","title":"Release process","text":"Warning
Under Construction
"},{"location":"design/configuration-management/repository-structure/","title":"Repository structure","text":""},{"location":"design/configuration-management/repository-structure/#repository-structure","title":"Repository structure","text":"Warning
Under Construction
"},{"location":"how-to-guides/","title":"How-to guides","text":""},{"location":"how-to-guides/#how-to-guides","title":"How-to guides","text":""},{"location":"how-to-guides/#integrating-autoware","title":"Integrating Autoware","text":"TODO: Write the following contents.
Warning
Under Construction
"},{"location":"how-to-guides/integrating-autoware/launch-autoware/","title":"5. Launch Autoware","text":""},{"location":"how-to-guides/integrating-autoware/launch-autoware/#launch-autoware","title":"Launch Autoware","text":"Warning
Under Construction
"},{"location":"how-to-guides/integrating-autoware/overview/","title":"Overview","text":""},{"location":"how-to-guides/integrating-autoware/overview/#overview","title":"Overview","text":""},{"location":"how-to-guides/integrating-autoware/overview/#requirement-prepare-your-real-vehicle-hardware","title":"Requirement: prepare your real vehicle hardware","text":"Prerequisites for the vehicle:
Create your Autoware meta-repository. One easy way is to fork autowarefoundation/autoware and clone it. For how to fork a repository, refer to GitHub Docs.
git clone https://github.com/YOUR_NAME/autoware.git\nIf you set up multiple types of vehicles, adding a suffix like \"autoware.vehicle_A\" or \"autoware.vehicle_B\" is recommended.
"},{"location":"how-to-guides/integrating-autoware/overview/#2-creating-the-your-vehicle-and-sensor-description","title":"2. Creating the your vehicle and sensor description","text":"Next, you need to create description packages that define the vehicle and sensor configuration of your vehicle.
Create the following two packages:
Once created, you need to update the autoware.repos file of your cloned Autoware repository to refer to these two description packages.
- # sensor_kit\n- sensor_kit/sample_sensor_kit_launch:\n- type: git\n- url: https://github.com/autowarefoundation/sample_sensor_kit_launch.git\n- version: main\n- # vehicle\n- vehicle/sample_vehicle_launch:\n- type: git\n- url: https://github.com/autowarefoundation/sample_vehicle_launch.git\n- version: main\n+ # sensor_kit\n+ sensor_kit/YOUR_SENSOR_KIT_launch:\n+ type: git\n+ url: https://github.com/YOUR_NAME/YOUR_SENSOR_KIT_launch.git\n+ version: main\n+ # vehicle\n+ vehicle/YOUR_VEHICLE_launch:\n+ type: git\n+ url: https://github.com/YOUR_NAME/YOUR_VEHICLE_launch.git\n+ version: main\nDefine URDF and parameters in the vehicle description package (refer to the sample vehicle description package for an example).
"},{"location":"how-to-guides/integrating-autoware/overview/#at-your_vehicle_launch","title":"At YOUR_VEHICLE_launch","text":"Create a launch file (refer to the sample vehicle launch package for example). If you have multiple vehicles with the same hardware setup, you can specify vehicle_id to distinguish them.
Define URDF and extrinsic parameters for all the sensors here (refer to the sample sensor kit description package for example). Note that you need to calibrate extrinsic parameters for all the sensors beforehand.
"},{"location":"how-to-guides/integrating-autoware/overview/#at-your_sensor_kit_launch","title":"At YOUR_SENSOR_KIT_launch","text":"Create launch/sensing.launch.xml that launches the interfaces of all the sensors on the vehicle. (refer to the sample sensor kit launch package for example).
Note
At this point, you are now able to run Autoware's Planning Simulator to do a basic test of your vehicle and sensing packages. To do so, you need to build and install Autoware using your cloned repository. Follow the steps for either Docker or source installation (starting from the dependency installation step) and then run the following command:
ros2 launch autoware_launch planning_simulator.launch.xml vehicle_model:=YOUR_VEHICLE sensor_kit:=YOUR_SENSOR_KIT map_path:=/PATH/TO/YOUR/MAP\nvehicle_interface package","text":"You need to create an interface package for your vehicle. The package is expected to provide the following two functions.
vehicle_cmd_gate and drive the vehicle accordinglyYou can find detailed information about the requirements of the vehicle_interface package in the Vehicle Interface design documentation. You can also refer to TIER IV's pacmod_interface repository as an example of a vehicle interface package.
You need both a pointcloud map and a vector map in order to use Autoware. For more information on map design, please click here.
"},{"location":"how-to-guides/integrating-autoware/overview/#create-a-pointcloud-map","title":"Create a pointcloud map","text":"Use third-party tools such as a LiDAR-based SLAM (Simultaneous Localization And Mapping) package to create a pointcloud map in the .pcd format. For more information, please click here.
Use third-party tools such as TIER IV's Vector Map Builder to create a Lanelet2 format .osm file.
This section briefly explains how to run your vehicle with Autoware.
"},{"location":"how-to-guides/integrating-autoware/overview/#install-autoware","title":"Install Autoware","text":"Follow the installation steps of Autoware.
"},{"location":"how-to-guides/integrating-autoware/overview/#launch-autoware","title":"Launch Autoware","text":"Launch Autoware with the following command:
ros2 launch autoware_launch autoware.launch.xml vehicle_model:=YOUR_VEHICLE sensor_kit:=YOUR_SENSOR_KIT map_path:=/PATH/TO/YOUR/MAP\nIf GNSS is available, Autoware automatically initializes the vehicle's pose.
If not, you need to set the initial pose using the RViz GUI.
Set a goal pose for the ego vehicle.
In your terminal, execute the following command.
source ~/autoware.YOURS/install/setup.bash\nros2 topic pub /autoware.YOURS/engage autoware_auto_vehicle_msgs/msg/Engage \"engage: true\" -1\nYou can also engage via RViz with \"AutowareStatePanel\". The panel can be found in Panels > Add New Panel > tier4_state_rviz_plugin > AutowareStatePanel.
Now the vehicle should drive along the calculated path!
"},{"location":"how-to-guides/integrating-autoware/overview/#6-tune-parameters-for-your-vehicle-environment","title":"6. Tune parameters for your vehicle & environment","text":"You may need to tune your parameters depending on the domain in which you will operate your vehicle.
"},{"location":"how-to-guides/integrating-autoware/creating-maps/","title":"Creating maps","text":""},{"location":"how-to-guides/integrating-autoware/creating-maps/#creating-maps","title":"Creating maps","text":"Autoware requires a pointcloud map and a vector map for the vehicle's operating environment. (Check the map design documentation page for the detailed specification).
This page explains how users can create maps that can be used for Autoware.
"},{"location":"how-to-guides/integrating-autoware/creating-maps/#creating-a-point-cloud-map","title":"Creating a point cloud map","text":"Traditionally, a Mobile Mapping System (MMS) is used in order to create highly accurate large-scale point cloud maps. However, since a MMS requires high-end sensors for precise positioning, its operational cost can be very expensive and may not be suitable for a relatively small driving environment. Alternatively, a Simultaneous Localization And Mapping (SLAM) algorithm can be used to create a point cloud map from recorded LiDAR scans. Some of the useful open-source SLAM implementations are listed in this page.
If you prefer proprietary software that is easy to use, you can try a fully automatic mapping tool from MAP IV, Inc., MapIV Engine. They currently provide a trial license for Autoware users free of charge.
"},{"location":"how-to-guides/integrating-autoware/creating-maps/#creating-a-vector-map","title":"Creating a vector map","text":"The easiest way to create an Autoware-compatible vector map is to use Vector Map Builder, a free web-based tool provided by TIER IV, Inc.. Vector Map Builder allows you to create lanes and add additional regulatory elements such as stop signs or traffic lights using a point cloud map as a reference.
For open-source software options, MapToolbox is a plugin for Unity specifically designed to create Lanelet2 maps for Autoware. Although JOSM is another open-source tool that can be used to create Lanelet2 maps, be aware that a number of modifications must be done manually to make the map compatible with Autoware. This process can be tedious and time-consuming, so the use of JOSM is not recommended.
"},{"location":"how-to-guides/integrating-autoware/creating-maps/#autoware-compatible-map-providers","title":"Autoware-compatible map providers","text":"If it is not possible to create HD maps yourself, you can use a mapping service from the following Autoware-compatible map providers instead:
The table below shows each company's mapping technology and the types of HD maps they support.
Company Mapping technology Available maps MAP IV, Inc. SLAM Point cloud and vector maps AISAN TECHNOLOGY CO., LTD. MMS Point cloud and vector maps TomTom MMS Vector map*Note
Maps provided by TomTom use their proprietary AutoStream format, not Lanelet2. The open-source AutoStreamForAutoware tool can be used to convert an AutoStream map to a Lanelet2 map. However, the converter is still in its early stages and has some known limitations.
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/","title":"Available Open Source SLAM","text":""},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/#available-open-source-slam","title":"Available Open Source SLAM","text":"This page provides the list of available open source Simultaneous Localization And Mapping (SLAM) implementation that can be used to generete a point cloud (.pcd) map file.
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/#selecting-which-implementation-to-use","title":"Selecting which implementation to use","text":"Lidar odometry drifts accumulatively as time goes by and there is solutions to solve that problem such as graph optimization, loop closure and using gps sensor to decrease accumulative drift error. Because of that, a SLAM algorithm should have loop closure feature, graph optimization and should use gps sensor. Additionally, some of the algorithms are using IMU sensor to add another factor to graph for decreasing drift error. While some of the algorithms requires 9-axis IMU sensor strictly, some of them requires only 6-axis IMU sensor or not even using the IMU sensor. Before choosing an algorithm to create maps for Autoware please consider these factors depends on your sensor setup or expected quality of generated map.
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/#tips","title":"Tips","text":"Commonly used open-source SLAM implementations are lidarslam-ros2 (LiDAR, IMU*) and LIO-SAM (LiDAR, IMU, GNSS). The required sensor data for each algorithm is specified in the parentheses, where an asterisk (*) indicates that such sensor data is optional. For supported LiDAR models, please check the Github repository of each algorithm. While these ROS 2-based SLAM implementations can be easily installed and used directly on the same machine that runs Autoware, it is important to note that they may not be as well-tested or as mature as ROS 1-based alternatives.
The notable open-source SLAM implementations that are based on ROS 1 include hdl-graph-slam (LiDAR, IMU*, GNSS*), LeGO-LOAM (LiDAR, IMU*), LeGO-LOAM-BOR (LiDAR), and LIO-SAM (LiDAR, IMU, GNSS).
Most of these algorithms already have a built-in loop-closure and pose graph optimization. However, if the built-in, automatic loop-closure fails or does not work correctly, you can use Interactive SLAM to adjust and optimize a pose graph manually.
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/#list-of-third-party-slam-implementations","title":"List of Third Party SLAM Implementations","text":"Package Name Explanation Repository Link Loop Closure Sensors ROS Version Dependencies FAST-LIO-LC A computationally efficient and robust LiDAR-inertial odometry package with loop closure module and graph optimization https://github.com/yanliang-wang/FAST_LIO_LC ✓ LidarIMUGPS [Optional] ROS1 ROS MelodicPCL >= 1.8Eigen >= 3.3.4GTSAM >= 4.0.0 FAST_LIO_SLAM FAST_LIO_SLAM is the integration of FAST_LIO and SC-PGO which is scan context based loop detection and GTSAM based pose-graph optimization https://github.com/gisbi-kim/FAST_LIO_SLAM ✓ LidarIMUGPS [Optional] ROS1 PCL >= 1.8Eigen >= 3.3.4 FD-SLAM FD_SLAM is Feature&Distribution-based 3D LiDAR SLAM method based on Surface Representation Refinement. In this algorithm novel feature-based Lidar odometry used for fast scan-matching, and used a proposed UGICP method for keyframe matching https://github.com/SLAMWang/FD-SLAM ✓ LidarIMU [Optional]GPS ROS1 PCLg2oSuitesparse hdl_graph_slam An open source ROS package for real-time 6DOF SLAM using a 3D LIDAR. It is based on 3D Graph SLAM with NDT scan matching-based odometry estimation and loop detection. It also supports several graph constraints, such as GPS, IMU acceleration (gravity vector), IMU orientation (magnetic sensor), and floor plane (detected in a point cloud) https://github.com/koide3/hdl_graph_slam ✓ LidarIMU [Optional]GPS [Optional] ROS1 PCLg2oOpenMP IA-LIO-SAM IA_LIO_SLAM is created for data acquisition in unstructured environment and it is a framework for Intensity and Ambient Enhanced Lidar Inertial Odometry via Smoothing and Mapping that achieves highly accurate robot trajectories and mapping https://github.com/minwoo0611/IA_LIO_SAM ✓ LidarIMUGPS ROS1 GTSAM ISCLOAM ISCLOAM presents a robust loop closure detection approach by integrating both geometry and intensity information https://github.com/wh200720041/iscloam ✓ Lidar ROS1 Ubuntu 18.04ROS MelodicCeresPCLGTSAMOpenCV LeGO-LOAM-BOR LeGO-LOAM-BOR is improved version of the LeGO-LOAM by improving quality of the code, making it more readable and consistent. Also, performance is improved by converting processes to multi-threaded approach https://github.com/facontidavide/LeGO-LOAM-BOR ✓ LidarIMU ROS1 ROS MelodicPCLGTSAM LIO_SAM A framework that achieves highly accurate, real-time mobile robot trajectory estimation and map-building. It formulates lidar-inertial odometry atop a factor graph, allowing a multitude of relative and absolute measurements, including loop closures, to be incorporated from different sources as factors into the system https://github.com/TixiaoShan/LIO-SAM ✓ LidarIMUGPS [Optional] ROS1ROS2 PCLGTSAM Optimized-SC-F-LOAM An improved version of F-LOAM and uses an adaptive threshold to further judge the loop closure detection results and reducing false loop closure detections. Also it uses feature point-based matching to calculate the constraints between a pair of loop closure frame point clouds and decreases time consumption of constructing loop frame constraints https://github.com/SlamCabbage/Optimized-SC-F-LOAM ✓ Lidar ROS1 PCLGTSAMCeres SC-A-LOAM A real-time LiDAR SLAM package that integrates A-LOAM and ScanContext. https://github.com/gisbi-kim/SC-A-LOAM ✓ Lidar ROS1 GTSAM >= 4.0 SC-LeGO-LOAM SC-LeGO-LOAM integrated LeGO-LOAM for lidar odometry and 2 different loop closure methods: ScanContext and Radius search based loop closure. While ScanContext is correcting large drifts, radius search based method is good for fine-stitching https://github.com/irapkaist/SC-LeGO-LOAM ✓ LidarIMU ROS1 PCLGTSAM"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/fast-lio-lc/","title":"FAST_LIO_LC","text":""},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/fast-lio-lc/#fast_lio_lc","title":"FAST_LIO_LC","text":""},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/fast-lio-lc/#what-is-fast_lio_lc","title":"What is FAST_LIO_LC?","text":"https://github.com/yanliang-wang/FAST_LIO_LC
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/fast-lio-lc/#required-sensors","title":"Required Sensors","text":" wget -O ~/Downloads/gtsam.zip https://github.com/borglab/gtsam/archive/4.0.0-alpha2.zip\n cd ~/Downloads/ && unzip gtsam.zip -d ~/Downloads/\n cd ~/Downloads/gtsam-4.0.0-alpha2/\n mkdir build && cd build\n cmake ..\n sudo make install\n mkdir -p ~/ws_fastlio_lc/src\n cd ~/ws_fastlio_lc/src\n git clone https://github.com/gisbi-kim/FAST_LIO_SLAM.git\n git clone https://github.com/Livox-SDK/livox_ros_driver\n cd ..\n catkin_make\nworkspace/src/FAST_LIO_LC/FAST_LIO/config/ouster64_mulran.yaml) with the lidar topic name in your bag file.pcd_save_enable must be 1 from the launch file (workspace/src/FAST_LIO_LC/FAST_LIO/launch/mapping_ouster64_mulran.launch).# open new terminal: run FAST-LIO\nroslaunch fast_lio mapping_ouster64.launch\n\n# open the other terminal tab: run SC-PGO\nroslaunch aloam_velodyne fastlio_ouster64.launch\n\n# play bag file in the other terminal\nrosbag play RECORDED_BAG.bag --clock\nCheck original repository link for example dataset.
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/fast-lio-lc/#contact","title":"Contact","text":"wyl410922@qq.com)https://github.com/gisbi-kim/FAST_LIO_SLAM
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/fast-lio-slam/#required-sensors","title":"Required Sensors","text":"wget -O ~/Downloads/gtsam.zip https://github.com/borglab/gtsam/archive/4.0.0-alpha2.zip\ncd ~/Downloads/ && unzip gtsam.zip -d ~/Downloads/\ncd ~/Downloads/gtsam-4.0.0-alpha2/\nmkdir build && cd build\ncmake ..\nsudo make install\n mkdir -p ~/catkin_fastlio_slam/src\n cd ~/catkin_fastlio_slam/src\n git clone https://github.com/gisbi-kim/FAST_LIO_SLAM.git\n git clone https://github.com/Livox-SDK/livox_ros_driver\n cd ..\n catkin_make\n source devel/setup.bash\nFast_LIO/config/ouster64.yaml # terminal 1: run FAST-LIO2\nroslaunch fast_lio mapping_ouster64.launch\n\n # open the other terminal tab: run SC-PGO\ncd ~/catkin_fastlio_slam\n source devel/setup.bash\n roslaunch aloam_velodyne fastlio_ouster64.launch\n\n # play bag file in the other terminal\nrosbag play xxx.bag -- clock --pause\nTutorial video 1 (using KAIST 03 sequence of MulRan dataset)
paulgkim@kaist.ac.kr)This is an open source ROS package for real-time 6DOF SLAM using a 3D LIDAR.
It is based on hdl_graph_slam and the steps to run our system are same with hdl-graph-slam.
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/fd-slam/#original-repository-link","title":"Original Repository link","text":"https://github.com/SLAMWang/FD-SLAM
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/fd-slam/#required-sensors","title":"Required Sensors","text":"The following ROS packages are required:
cd ~/catkin_ws/src\ngit clone https://github.com/SLAMWang/FD-SLAM.git\ncd ..\ncatkin_make\n/hdl_graph_slam/dump (hdl_graph_slam/DumpGraph)\n- save all the internal data (point clouds, floor coeffs, odoms, and pose graph) to a directory.\n\n/hdl_graph_slam/save_map (hdl_graph_slam/SaveMap)\n- save the generated map as a PCD file.\nsource devel/setup.bash\nroslaunch hdl_graph_slam hdl_graph_slam_400_ours.launch\nhttps://github.com/koide3/hdl_graph_slam
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/hdl-graph-slam/#required-sensors","title":"Required Sensors","text":"The following ROS packages are required:
# for melodic\nsudo apt-get install ros-melodic-geodesy ros-melodic-pcl-ros ros-melodic-nmea-msgs ros-melodic-libg2o\ncd catkin_ws/src\ngit clone https://github.com/koide3/ndt_omp.git -b melodic\ngit clone https://github.com/SMRT-AIST/fast_gicp.git --recursive\ngit clone https://github.com/koide3/hdl_graph_slam\n\ncd .. && catkin_make -DCMAKE_BUILD_TYPE=Release\n\n# for noetic\nsudo apt-get install ros-noetic-geodesy ros-noetic-pcl-ros ros-noetic-nmea-msgs ros-noetic-libg2o\n\ncd catkin_ws/src\ngit clone https://github.com/koide3/ndt_omp.git\ngit clone https://github.com/SMRT-AIST/fast_gicp.git --recursive\ngit clone https://github.com/koide3/hdl_graph_slam\n\ncd .. && catkin_make -DCMAKE_BUILD_TYPE=Release\nlaunch/hdl_graph_slam_400.launchlaunch/hdl_graph_slam_400.launchrosparam set use_sim_time true\nroslaunch hdl_graph_slam hdl_graph_slam_400.launch\nroscd hdl_graph_slam/rviz\nrviz -d hdl_graph_slam.rviz\nrosbag play --clock hdl_400.bag\nSave the generated map by:
rosservice call /hdl_graph_slam/save_map \"resolution: 0.05\ndestination: '/full_path_directory/map.pcd'\"\nBag file (recorded in an outdoor environment):
rosparam set use_sim_time true\nroslaunch hdl_graph_slam hdl_graph_slam_400.launch\nroscd hdl_graph_slam/rviz\nrviz -d hdl_graph_slam.rviz\nrosbag play --clock dataset.bag\n
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/hdl-graph-slam/#papers","title":"Papers","text":"
Kenji Koide, Jun Miura, and Emanuele Menegatti, A Portable 3D LIDAR-based System for Long-term and Wide-area People Behavior Measurement, Advanced Robotic Systems, 2019 [link].
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/hdl-graph-slam/#contact","title":"Contact","text":"Kenji Koide, k.koide@aist.go.jp, https://staff.aist.go.jp/k.koide
[Active Intelligent Systems Laboratory, Toyohashi University of Technology, Japan] [Mobile Robotics Research Team, National Institute of Advanced Industrial Science and Technology (AIST), Japan]
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/ia-lio-slam/","title":"IA-LIO-SAM","text":""},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/ia-lio-slam/#ia-lio-sam","title":"IA-LIO-SAM","text":""},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/ia-lio-slam/#what-is-ia-lio-sam","title":"What is IA-LIO-SAM?","text":"https://github.com/minwoo0611/IA_LIO_SAM
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/ia-lio-slam/#required-sensors","title":"Required Sensors","text":"ROS (tested with Kinetic and Melodic)
for ROS melodic:
sudo apt-get install -y ros-melodic-navigation\nsudo apt-get install -y ros-melodic-robot-localization\nsudo apt-get install -y ros-melodic-robot-state-publisher\nfor ROS kinetic:
sudo apt-get install -y ros-kinetic-navigation\nsudo apt-get install -y ros-kinetic-robot-localization\nsudo apt-get install -y ros-kinetic-robot-state-publisher\nGTSAM (Georgia Tech Smoothing and Mapping library)
wget -O ~/Downloads/gtsam.zip https://github.com/borglab/gtsam/archive/4.0.2.zip\ncd ~/Downloads/ && unzip gtsam.zip -d ~/Downloads/\ncd ~/Downloads/gtsam-4.0.2/\nmkdir build && cd build\ncmake -DGTSAM_BUILD_WITH_MARCH_NATIVE=OFF ..\nsudo make install -j8\n mkdir -p ~/catkin_ia_lio/src\n cd ~/catkin_ia_lio/src\n git clone https://github.com/minwoo0611/IA_LIO_SAM\n cd ..\n catkin_make\nworkspace/src/IA_LIO_SAM/config/params.yaml)savePCD must be true on the params.yaml file (workspace/src/IA_LIO_SAM/config/params.yaml). # open new terminal: run IA_LIO\n source devel/setup.bash\n roslaunch lio_sam mapping_ouster64.launch\n\n # play bag file in the other terminal\n rosbag play RECORDED_BAG.bag --clock\nCheck original repo link for example dataset.
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/ia-lio-slam/#contact","title":"Contact","text":"Github: minwoo0611)Thank you for citing IA-LIO-SAM(./config/doc/KRS-2021-17.pdf) if you use any of this code.
Part of the code is adapted from LIO-SAM (IROS-2020).
@inproceedings{legoloam2018shan,\n title={LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain},\n author={Shan, Tixiao and Englot, Brendan},\n booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},\n pages={4758-4765},\n year={2018},\n organization={IEEE}\n}\nhttps://github.com/wh200720041/iscloam
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/iscloam/#required-sensors","title":"Required Sensors","text":"For visualization purpose, this package uses hector trajectory sever, you may install the package by
sudo apt-get install ros-melodic-hector-trajectory-server\ncd ~/catkin_ws/src\ngit clone https://github.com/wh200720041/iscloam.git\ncd ..\ncatkin_make -j1\nsource ~/catkin_ws/devel/setup.bash\nChange the bag location and sensor parameters on launch files.
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/iscloam/#3-launch","title":"3. Launch","text":"roslaunch iscloam iscloam.launch\nif you would like to generate the map of environment at the same time, you can run
roslaunch iscloam iscloam_mapping.launch\nNote that the global map can be very large, so it may takes a while to perform global optimization, some lag is expected between trajectory and map since they are running in separate thread. More CPU usage will happen when loop closure is identified.
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/iscloam/#example-result","title":"Example Result","text":"Watch demo video at Video Link
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/iscloam/#ground-truth-comparison","title":"Ground Truth Comparison","text":"Green: ISCLOAM Red: Ground Truth
KITTI sequence 00 KITTI sequence 05\nIf you use this work for your research, you may want to cite the paper below, your citation will be appreciated
@inproceedings{wang2020intensity,\n author={H. {Wang} and C. {Wang} and L. {Xie}},\n booktitle={2020 IEEE International Conference on Robotics and Automation (ICRA)},\n title={Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection},\n year={2020},\n volume={},\n number={},\n pages={2095-2101},\n doi={10.1109/ICRA40945.2020.9196764}\n}\nThanks for A-LOAM and LOAM(J. Zhang and S. Singh. LOAM: Lidar Odometry and Mapping in Real-time) and LOAM_NOTED.
Author: Wang Han, Nanyang Technological University, Singapore
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/lego-loam-bor/","title":"LeGO-LOAM-BOR","text":""},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/lego-loam-bor/#lego-loam-bor","title":"LeGO-LOAM-BOR","text":""},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/lego-loam-bor/#what-is-lego-loam-bor","title":"What is LeGO-LOAM-BOR?","text":"https://github.com/facontidavide/LeGO-LOAM-BOR
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/lego-loam-bor/#required-sensors","title":"Required Sensors","text":"wget -O ~/Downloads/gtsam.zip https://github.com/borglab/gtsam/archive/4.0.0-alpha2.zip\ncd ~/Downloads/ && unzip gtsam.zip -d ~/Downloads/\ncd ~/Downloads/gtsam-4.0.0-alpha2/\nmkdir build && cd build\ncmake ..\nsudo make install\ncd ~/catkin_ws/src\ngit clone https://github.com/facontidavide/LeGO-LOAM-BOR.git\ncd ..\ncatkin_make\nLeGo-LOAM/loam_config.yamlsource devel/setup.bash\nroslaunch lego_loam_bor run.launch rosbag:=/path/to/your/rosbag lidar_topic:=/velodyne_points\nThank you for citing our LeGO-LOAM paper if you use any of this code:
@inproceedings{legoloam2018,\n title={LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain},\n author={Tixiao Shan and Brendan Englot},\n booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},\n pages={4758-4765},\n year={2018},\n organization={IEEE}\n}\nhttps://github.com/TixiaoShan/LIO-SAM
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/lio-sam/#required-sensors","title":"Required Sensors","text":"Gtsam (Georgia Tech Smoothing and Mapping library)
sudo add-apt-repository ppa:borglab/gtsam-release-4.0\nsudo apt install libgtsam-dev libgtsam-unstable-dev\n sudo apt-get install -y ros-melodic-navigation\n sudo apt-get install -y ros-melodic-robot-localization\n sudo apt-get install -y ros-melodic-robot-state-publisher\n mkdir -p ~/catkin_lio_sam/src\n cd ~/catkin_lio_sam/src\n git clone https://github.com/TixiaoShan/LIO-SAM.git\n cd ..\n catkin_make\n source devel/setup.bash\nlio_sam/config/params.yaml # Run the Launch File\nroslaunch lio_sam run.launch\n\n # Play bag file in the other terminal\nrosbag play xxx.bag --clock\nThank you for citing LIO-SAM (IROS-2020) if you use any of this code.
@inproceedings{liosam2020shan,\n title={LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping},\n author={Shan, Tixiao and Englot, Brendan and Meyers, Drew and Wang, Wei and Ratti, Carlo and Rus Daniela},\n booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},\n pages={5135-5142},\n year={2020},\n organization={IEEE}\n}\nPart of the code is adapted from LeGO-LOAM.
@inproceedings{legoloam2018shan,\n title={LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain},\n author={Shan, Tixiao and Englot, Brendan},\n booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},\n pages={4758-4765},\n year={2018},\n organization={IEEE}\n}\nhttps://github.com/SlamCabbage/Optimized-SC-F-LOAM
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/optimized-sc-f-loam/#required-sensors","title":"Required Sensors","text":"sudo apt-get install ros-noetic-hector-trajectory-server\ncd ~/catkin_ws/src\ngit clone https://github.com/SlamCabbage/Optimized-SC-F-LOAM.git\ncd ..\ncatkin_make\nIn this folder, Ground Truth information, optimized pose information, F-LOAM pose information and time information are stored
mkdir -p ~/message/Scans\n\nChange line 383 in the laserLoopOptimizationNode.cpp to your own \"message\" folder path\n(Do not forget to rebuild your package)
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/optimized-sc-f-loam/#3-set-parameters","title":"3) Set parameters","text":"source devel/setup.bash\nroslaunch optimized_sc_f_loam optimized_sc_f_loam_mapping.launch\nTest on KITTI sequence You can download the sequence 00 and 05 datasets from the KITTI official website and convert them into bag files using the kitti2bag open source method.
00: 2011_10_03_drive_0027 000000 004540
05: 2011_09_30_drive_0018 000000 002760
See the link: https://github.com/ethz-asl/kitti_to_rosbag
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/optimized-sc-f-loam/#acknowledgements","title":"Acknowledgements","text":"Thanks for SC-A-LOAM(Scan context: Egocentric spatial descriptor for place recognition within 3d point cloud map) and F-LOAM(F-LOAM : Fast LiDAR Odometry and Mapping).
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/optimized-sc-f-loam/#citation","title":"Citation","text":"@misc{https://doi.org/10.48550/arxiv.2204.04932,\n doi = {10.48550/ARXIV.2204.04932},\n\n url = {https://arxiv.org/abs/2204.04932},\n\n author = {Liao, Lizhou and Fu, Chunyun and Feng, Binbin and Su, Tian},\n\n keywords = {Robotics (cs.RO), FOS: Computer and information sciences, FOS: Computer and information sciences},\n\n title = {Optimized SC-F-LOAM: Optimized Fast LiDAR Odometry and Mapping Using Scan Context},\n\n publisher = {arXiv},\n\n year = {2022},\n\n copyright = {arXiv.org perpetual, non-exclusive license}\n}\nhttps://github.com/gisbi-kim/SC-A-LOAM
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/sc-a-loam/#required-sensors","title":"Required Sensors","text":"If GTSAM is not installed, follow the steps below.
wget -O ~/Downloads/gtsam.zip https://github.com/borglab/gtsam/archive/4.0.2.zip\n cd ~/Downloads/ && unzip gtsam.zip -d ~/Downloads/\n cd ~/Downloads/gtsam-4.0.2/\n mkdir build && cd build\n cmake -DGTSAM_BUILD_WITH_MARCH_NATIVE=OFF ..\n sudo make install -j8\nFirst, install the abovementioned dependencies and follow below lines.
mkdir -p ~/catkin_scaloam_ws/src\n cd ~/catkin_scaloam_ws/src\n git clone https://github.com/gisbi-kim/SC-A-LOAM.git\n cd ../\n catkin_make\n source ~/catkin_scaloam_ws/devel/setup.bash\nroslaunch aloam_velodyne aloam_mulran.launch\n rosrun pcl_ros pointcloud_to_pcd input:=/aft_pgo_map\nexample videos on Riverside 01 sequence.
1. with consumer level GPS-based altitude stabilization: https://youtu.be/FwAVX5TVm04\n2. without the z stabilization: https://youtu.be/okML_zNadhY\nexample result:
For KITTI (HDL-64 sensor), run using the command
roslaunch aloam_velodyne aloam_velodyne_HDL_64.launch # for KITTI dataset setting\nexample result:
https://github.com/irapkaist/SC-LeGO-LOAM
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/sc-lego-loam/#required-sensors","title":"Required Sensors","text":"wget -O ~/Downloads/gtsam.zip https://github.com/borglab/gtsam/archive/4.0.0-alpha2.zip\ncd ~/Downloads/ && unzip gtsam.zip -d ~/Downloads/\ncd ~/Downloads/gtsam-4.0.0-alpha2/\nmkdir build && cd build\ncmake ..\nsudo make install\ncd ~/catkin_ws/src\ngit clone https://github.com/irapkaist/SC-LeGO-LOAM.git\ncd ..\ncatkin_make\ninclude/utility.hinclude/utility.hinclude/Scancontext.h(Do not forget to rebuild after setting parameters.)
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/sc-lego-loam/#3-run","title":"3) Run","text":"source devel/setup.bash\nroslaunch lego_loam run.launch\n@INPROCEEDINGS { gkim-2018-iros,\n author = {Kim, Giseop and Kim, Ayoung},\n title = { Scan Context: Egocentric Spatial Descriptor for Place Recognition within {3D} Point Cloud Map },\n booktitle = { Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems },\n year = { 2018 },\n month = { Oct. },\n address = { Madrid }\n}\nand
@inproceedings{legoloam2018,\n title={LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain},\n author={Shan, Tixiao and Englot, Brendan},\n booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},\n pages={4758-4765},\n year={2018},\n organization={IEEE}\n}\npaulgkim@kaist.ac.kr)Autoware expects to have multiple sensors attached to the vehicle as input to perception, localization, and planning stack. These sensors must be calibrated correctly and their positions must be defined using either urdf files (as in sample_sensor_kit) or as tf launch files.
"},{"location":"how-to-guides/integrating-autoware/creating-vehicle-and-sensor-description/calibrating-sensors/#camera-calibration","title":"Camera calibration","text":""},{"location":"how-to-guides/integrating-autoware/creating-vehicle-and-sensor-description/calibrating-sensors/#intrinsic-calibration","title":"Intrinsic Calibration","text":"LL-Calib on Github, provided by AutoCore, is a lightweight toolkit for online/offline 3D LiDAR to LiDAR calibration. It's based on local mapping and \"GICP\" method to derive the relation between main and sub lidar. Information on how to use the tool, troubleshooting tips and example rosbags can be found at the above link.
"},{"location":"how-to-guides/integrating-autoware/creating-vehicle-and-sensor-description/calibrating-sensors/#lidar-camera-calibration","title":"Lidar-camera calibration","text":"Developed by MathWorks, The Lidar Camera Calibrator app enables you to interactively estimate the rigid transformation between a lidar sensor and a camera.
https://ww2.mathworks.cn/help/lidar/ug/get-started-lidar-camera-calibrator.html
SensorsCalibration toolbox v0.1: One more open source method for Lidar-camera calibration. This is a project for LiDAR to camera calibration,including automatic calibration and manual calibration
https://github.com/PJLab-ADG/SensorsCalibration/blob/master/lidar2camera/README.md
Developed by AutoCore, an easy-to-use lightweight toolkit for Lidar-camera-calibration is proposed. Only in three steps, a fully automatic calibration will be done.
https://github.com/autocore-ai/calibration_tools/tree/main/lidar-cam-calib-related
"},{"location":"how-to-guides/integrating-autoware/creating-vehicle-and-sensor-description/calibrating-sensors/#lidar-imu-calibration","title":"Lidar-IMU calibration","text":"Developed by APRIL Lab at Zhejiang University in China, the LI-Calib calibration tool is a toolkit for calibrating the 6DoF rigid transformation and the time offset between a 3D LiDAR and an IMU, based on continuous-time batch optimization. IMU-based cost and LiDAR point-to-surfel (surfel = surface element) distance are minimized jointly, which renders the calibration problem well-constrained in general scenarios.
AutoCore has forked the original LI-Calib tool and overwritten the Lidar input for more general usage. Information on how to use the tool, troubleshooting tips and example rosbags can be found at the LI-Calib fork on Github.
"},{"location":"how-to-guides/integrating-autoware/creating-vehicle-and-sensor-description/creating-vehicle-and-sensor-description/","title":"Creating vehicle and sensor description","text":""},{"location":"how-to-guides/integrating-autoware/creating-vehicle-and-sensor-description/creating-vehicle-and-sensor-description/#creating-vehicle-and-sensor-description","title":"Creating vehicle and sensor description","text":"Warning
Under Construction
"},{"location":"how-to-guides/integrating-autoware/creating-vehicle-interface-package/creating-vehicle-interface-for-ackerman-kinematic-model/","title":"Creating vehicle interface for ackerman kinematic model","text":""},{"location":"how-to-guides/integrating-autoware/creating-vehicle-interface-package/creating-vehicle-interface-for-ackerman-kinematic-model/#creating-vehicle-interface-for-ackerman-kinematic-model","title":"Creating vehicle interface for ackerman kinematic model","text":"Warning
Under Construction
"},{"location":"how-to-guides/integrating-autoware/creating-vehicle-interface-package/customizing-for-differential-drive-model/","title":"Customizing for differential drive vehicle","text":""},{"location":"how-to-guides/integrating-autoware/creating-vehicle-interface-package/customizing-for-differential-drive-model/#customizing-for-differential-drive-vehicle","title":"Customizing for differential drive vehicle","text":""},{"location":"how-to-guides/integrating-autoware/creating-vehicle-interface-package/customizing-for-differential-drive-model/#1-introduction","title":"1. Introduction","text":"Currently, Autoware assumes that vehicles use an Ackermann kinematic model with Ackermann steering. Thus, Autoware adopts the Ackermann command format for the Control module's output (see the AckermannDrive ROS message definition for an overview of Ackermann commands, and the AckermannControlCommands struct used in Autoware for more details).
However, it is possible to integrate Autoware with a vehicle that follows a differential drive kinematic model, as commonly used by small mobile robots.
"},{"location":"how-to-guides/integrating-autoware/creating-vehicle-interface-package/customizing-for-differential-drive-model/#2-procedure","title":"2. Procedure","text":"One simple way of using Autoware with a differential drive vehicle is to create a vehicle_interface package that translates Ackermann commands to differential drive commands. Here are two points that you need to consider:
vehicle_interface package for differential drive vehiclewheel_basevehicle_interface package for differential drive vehicle","text":"An Ackermann command in Autoware consists of two main control inputs:
Conversely, a typical differential drive command consists of the following inputs:
So, one way in which an Ackermann command can be converted to a differential drive command is by using the following equations:
v_l = v - \\frac{l\\omega}{2}, v_r = v + \\frac{l\\omega}{2}where l denotes wheel tread.
For information about other factors that need to be considered when creating a vehicle_interface package, refer to the vehicle_interface component page.
wheel_base","text":"A differential drive robot does not necessarily have front and rear wheels, which means that the wheelbase (the horizontal distance between the axles of the front and rear wheels) cannot be defined. However, Autoware expects wheel_base to be set in vehicle_info.param.yaml with some value. Thus, you need to set a pseudo value for wheel_base.
The appropriate pseudo value for wheel_base depends on the size of your vehicle. Setting it to be the same value as wheel_tread is one possible choice.
Warning
wheel_base value is set too small then the vehicle may behave unexpectedly. For example, the vehicle may drive beyond the bounds of a calculated path.wheel_base is set too large, the vehicle's range of motion will be restricted. The reason being that Autoware's Planning module will calculate an overly conservative trajectory based on the assumed vehicle length.Since Autoware assumes that vehicles use a steering system, it is not possible to take advantage of the flexibility of a differential drive system's motion model.
For example, when planning a parking maneuver with the freespace_planner module, Autoware may drive the differential drive vehicle forward and backward, even if the vehicle can be parked with a simpler trajectory that uses pure rotational movement.
This page shows how to use control_performance_analysis package to evaluate the controllers.
control_performance_analysis is the package to analyze the tracking performance of a control module and monitor the driving status of the vehicle.
If you need more detailed information about package, refer to the control_performance_analysis.
"},{"location":"how-to-guides/integrating-autoware/tuning-parameters-and-performance/evaluating-controller-performance/#how-to-use","title":"How to use","text":""},{"location":"how-to-guides/integrating-autoware/tuning-parameters-and-performance/evaluating-controller-performance/#before-driving","title":"Before Driving","text":""},{"location":"how-to-guides/integrating-autoware/tuning-parameters-and-performance/evaluating-controller-performance/#1-firstly-you-need-to-launch-autoware-you-can-also-use-this-tool-with-real-vehicle-driving","title":"1. Firstly you need to launch Autoware. You can also use this tool with real vehicle driving","text":""},{"location":"how-to-guides/integrating-autoware/tuning-parameters-and-performance/evaluating-controller-performance/#2-initialize-the-vehicle-and-send-goal-position-to-create-route","title":"2. Initialize the vehicle and send goal position to create route","text":"ros2 launch control_performance_analysis controller_performance_analysis.launch.xml\nsource ~/autoware/install/setup.bash\nros2 run plotjuggler plotjuggler\n/autoware.universe/control/control_performance_analysis/config/controller_monitor.xml","text":"If present, use the timestamp in the field [header.stamp] box, then select the OK..cvs file from data section.Help -> Cheatsheet in PlotJuggler to see more tips about it.odom_interval and low_pass_filter_gain from here to avoid noised data.Autoware should be real-time system when integrated to a service. Therefore, the response time of each callback should be as small as possible. If Autoware appears to be slow, it is imperative to conduct performance measurements and implement improvements based on the analysis. However, Autoware is a complex software system comprising numerous ROS 2 nodes, potentially complicating the process of identifying bottlenecks. To address this challenge, we will discuss methods for conducting detailed performance measurements for Autoware and provide case studies. It is worth noting that multiple factors can contribute to poor performance, such as scheduling and memory allocation in the OS layer, but our focus in this page will be on user code bottlenecks. The outline of this section is as follows:
Improvement is impossible without precise measurements. To measure the performance of the application code, it is essential to eliminate any external influences. Such influences include interference from the operating system and CPU frequency fluctuations. Scheduling effects also occur when core resources are shared by multiple threads. This section outlines a technique for accurately measuring the performance of the application code for a specific node. Though this section only discusses the case of Linux on Intel CPUs, similar considerations should be made in other environments.
"},{"location":"how-to-guides/integrating-autoware/tuning-parameters-and-performance/evaluating-real-time-performance/#single-node-execution","title":"Single node execution","text":"To eliminate the influence of scheduling, the node being measured should operate independently, using the same logic as when the entire Autoware system is running. To accomplish this, record all input topics of the node to be measured while the whole Autoware system is running. To achieve this objective, a tool called ros2_single_node_replayer has been prepared.
Details on how to use the tool can be found in the README. This tool records the input topics of a specific node during the entire Autoware operation and replays it in a single node with the same logic. The tool relies on the ros2 bag record command, and the recording of service/action is not supported as of ROS 2 Humble, so nodes that use service/action as their main logic may not work well.
Isolated cores running the node to be measured must meet the following conditions.
To fulfill these conditions on Linux, a custom kernel build with the following kernel configurations is required. You can find many resources to instruct you on how to build a custom Linux kernel (like this one). Note that even if full tickless is enabled, timer interrupts are generated for scheduling if more than two tasks exist in one core.
# Enable CONFIG_NO_HZ_FULL\n-> General setup\n-> Timers subsystem\n-> Timer tick handling (Full dynticks system (tickless))\n(X) Full dynticks system (tickless)\n\n# Allows RCU callback processing to be offloaded from selected CPUs\n# (CONFIG_RCU_NOCB_CPU=y)\n-> General setup\n-> RCU Subsystem\n-*- Offload RCU callback processing from boot-selected CPUs\nAdditionally, the kernel boot parameters need to be set as follows.
GRUB_CMDLINE_LINUX_DEFAULT=\n \"... isolcpus=2,8 rcu_nocbs=2,8 rcu_nocb_poll nohz_full=2,8 intel_pstate=disable\u201d\nIn the above configuration, for example, the node to be measured is assumed to run on core 2, and core 8, which is a hyper-threading pair, is also being isolated. Appropriate decisions on which cores to run the measurement target and which nodes to isolate need to be made based on the cache and core layout of the measurement machine. You can easily check if it is properly configured by running cat /proc/softirqs. Since intel_pstate=disable is specified in the kernel boot parameter, userspace can be specified in the scaling governor.
cat /sys/devices/system/cpu/cpu2/cpufreq/scaling_governor // ondemand\nsudo sh -c \"echo userspace > /sys/devices/system/cpu/cpu2/cpufreq/scaling_governor\"\nThis allows you to freely set the desired frequency within a defined range.
sudo sh -c \"echo <freq(kz)> > /sys/devices/system/cpu/cpu2/cpufreq/scaling_setspeed\"\nTurbo Boost needs to be switched off on Intel CPUs, which is often overlooked.
sudo sh -c \"echo 0 > /sys/devices/system/cpu/cpufreq/boost\"\nFollowing the instructions in the ros2_single_node_replayer README, start the node and play the dedicated rosbag created by the tool. Before playing the rosbag, appropriately set the CPU affinity of the thread on which the node runs, so it is placed on the isolated core prepared.
taskset --cpu-list -p <target cpu> <pid>\nTo avoid interference in the last level cache, minimize the number of other applications running during the measurement.
"},{"location":"how-to-guides/integrating-autoware/tuning-parameters-and-performance/evaluating-real-time-performance/#measurement-and-visualization","title":"Measurement and visualization","text":"To visualize the performance of the measurement target, embed code for logging timestamps and performance counter values in the target source code. To achieve this objective, a tool called pmu_analyzer has been prepared.
Details on how to use the tool can be found in the README. This tool can measure the turnaround time of any section in the source code, as well as various performance counters.
"},{"location":"how-to-guides/integrating-autoware/tuning-parameters-and-performance/evaluating-real-time-performance/#case-studies","title":"Case studies","text":"In this section, we will present several case studies that demonstrate the performance improvements. These examples not only showcase our commitment to enhancing the system's efficiency but also serve as a valuable resource for developers who may face similar challenges in their own projects. The performance improvements discussed here span various components of the Autoware system, including sensing modules and planning modules. There are tendencies for each component regarding which points are becoming bottlenecks. By examining the methods, techniques, and tools employed in these case studies, readers can gain a better understanding of the practical aspects of optimizing complex software systems like Autoware.
"},{"location":"how-to-guides/integrating-autoware/tuning-parameters-and-performance/evaluating-real-time-performance/#sensing-component","title":"Sensing component","text":"First, we will explain the procedure for performance improvement, taking the node ring_outlier_filter as an example. Refer to the Pull Request for details.
The following figure is a time-series plot of the turnaround time of the main processing part of ring_outlier_filter, analyzed as described in the \"Performance Measurement\" section above.
The horizontal axis indicates the number of callbacks called (i.e., callback index), and the vertical axis indicates the turnaround time.
When analyzing the performance of the sensing module from the viewpoint of performance counter, pay attention to instructions, LLC-load-misses, LLC-store-misses, cache-misses, and minor-faults.
Analysis of the performance counter shows that the largest fluctuations come from minor-faults (i.e., soft page faults), the second largest from LLC-store-misses and LLC-load-misses (i.e., cache misses in the last level cache), and the slowest fluctuations come from instructions (i.e., message data size fluctuations). For example, when we plot minor-faults on the horizontal axis and turnaround time on the vertical axis, we can see the following dominant proportional relationship.
To achieve zero soft page faults, heap allocations must only be made from areas that have been first touched in advance. We have developed a library called heaphook to avoid soft page faults while running Autoware callback. If you are interested, refer to the GitHub discussion and the issue.
To reduce LLC misses, it is necessary to reduce the working set and to use cache-efficient access patterns.
In the sensing component, which handles large message data such as LiDAR point cloud data, minimizing copying is important. A callback that takes sensor data message types as input and output should be written in an in-place algorithm as much as possible. This means that in the following pseudocode, when generating output_msg from input_msg, it is crucial to avoid using buffers as much as possible to reduce the number of memory copies.
void callback(const PointCloudMsg &input_msg) {\nauto output_msg = allocate_msg<PointCloudMsg>(output_size);\nfill(input_msg, output_msg);\npublish(std::move(output_msg));\n}\nTo improve cache efficiency, implement an in-place style as much as possible, instead of touching memory areas sporadically. In ROS applications using PCL, the code shown below is often seen.
void callback(const sensor_msgs::PointCloud2ConstPtr &input_msg) {\npcl::PointCloud<PointT>::Ptr input_pcl(new pcl::PointCloud<PointT>);\npcl::fromROSMsg(*input_msg, *input_pcl);\n\n// Algorithm is described for point cloud type of pcl\npcl::PointCloud<PointT>::Ptr output_pcl(new pcl::PointCloud<PointT>);\nfill_pcl(*input_pcl, *output_pcl);\n\nauto output_msg = allocate_msg<sensor_msgs::PointCloud2>(output_size);\npcl::toROSMsg(*output_pcl, *output_msg);\npublish(std::move(output_msg));\n}\nTo use the PCL library, fromROSMsg() and toROSMsg() are used to perform message type conversion at the beginning and end of the callback. This is a wasteful copying process and should be avoided. We should eliminate unnecessary type conversions by removing dependencies on PCL (e.g., https://github.com/tier4/velodyne_vls/pull/39). For large message types such as map data, there should be only one instance in the entire system in terms of physical memory.
First, we will pick up detection_area module in behavior_velocity_planner node, which tends to have long turnaround time. We have followed the performance analysis steps above to obtain the following graph. Axises are the same as the graphs in the sensing case study.
Using pmu_analyzer tool to further identify the bottleneck, we have found that the following multiple loops were taking up a lot of processing time:
for ( area : detection_areas )\nfor ( point : point_clouds )\nif ( boost::geometry::within(point, area) )\n// do something with O(1)\nIt checks whether each point cloud is contained in each detection area. Let N be the size of point_clouds and M be the size of detection_areas, then the computational complexity of this program is O(N^2 * M), since the complexity of within is O(N). Here, given that most of the point clouds are located far away from a certain detection area, a certain optimization can be achieved. First, calculate the minimum enclosing circle that completely covers the detection area, and then check whether the points are contained in that circle. Most of the point clouds can be quickly ruled out by this method, we don\u2019t have to call the within function in most cases. Below is the pseudocode after optimization.
for ( area : detection_areas )\ncircle = calc_minimum_enclosing_circle(area)\nfor ( point : point_clouds )\nif ( point is in circle )\nif ( boost::geometry::within(point, area) )\n// do something with O(1)\nBy using O(N) algorithm for minimum enclosing circle, the computational complexity of this program is reduced to almost O(N * (N + M)) (note that the exact computational complexity does not really change). If you are interested, refer to the Pull Request.
Similar to this example, in the planning component, we take into consideration thousands to tens of thousands of point clouds, thousands of points in a path representing our own route, and polygons representing obstacles and detection areas in the surroundings, and we repeatedly create paths based on them. Therefore, we access the contents of the point clouds and paths multiple times using for-loops. In most cases, the bottleneck lies in these naive for-loops. Here, understanding Big O notation and reducing the order of computational complexity directly leads to performance improvements.
"},{"location":"how-to-guides/others/add-a-custom-ros-message/","title":"Add a custom ROS message","text":""},{"location":"how-to-guides/others/add-a-custom-ros-message/#add-a-custom-ros-message","title":"Add a custom ROS message","text":""},{"location":"how-to-guides/others/add-a-custom-ros-message/#overview","title":"Overview","text":"During the Autoware development, you will probably need to define your own messages. Read the following instructions before adding a custom message.
Message in autoware_msgs define interfaces of Autoware Core.
autoware_msgs, they should first create a new discussion post under the Design category.Any other minor or proposal messages used for internal communication within a component(such as planning) should be defined in another repository.
The following is a simple tutorial of adding a message package to autoware_msgs. For the general ROS2 tutorial, see Create custom msg and srv files.
Make sure you are in the Autoware workspace, and then run the following command to create a new package. As an example, let's create a package to define sensor messages.
Create a package
cd ./src/core/autoware_msgs\nros2 pkg create --build-type ament_cmake autoware_sensing_msgs\nCreate custom messages
You should create .msg files and place them in the msg directory.
NOTE: The initial letters of the .msg and .srv files must be capitalized.
As an example, let's make .msg files GnssInsOrientation.msg and GnssInsOrientationStamped.msg to define GNSS/INS orientation messages:
mkdir msg\ncd msg\ntouch GnssInsOrientation.msg\ntouch GnssInsOrientationStamped.msg\nEdit GnssInsOrientation.msg with your editor to be the following content:
geometry_msgs/Quaternion orientation\nfloat32 rmse_rotation_x\nfloat32 rmse_rotation_y\nfloat32 rmse_rotation_z\nIn this case, the custom message uses a message from another message package geometry_msgs/Quaternion.
Edit GnssInsOrientationStamped.msg with your editor to be the following content:
std_msgs/Header header\nGnssInsOrientation orientation\nIn this case, the custom message uses a message from another message package std_msgs/Header.
Edit CMakeLists.txt
In order to use this custom message in C++ or Python languages, we need to add the following lines to CMakeList.txt:
rosidl_generate_interfaces(${PROJECT_NAME}\n\"msg/GnssInsOrientation.msg\"\n\"msg/GnssInsOrientationStamped.msg\"\nDEPENDENCIES\ngeometry_msgs\nstd_msgs\nADD_LINTER_TESTS\n)\n The ament_cmake_auto tool is very useful and is more widely used in Autoware, so we recommend using ament_cmake_auto instead of ament_cmake.
We need to replace
find_package(ament_cmake REQUIRED)\n\nament_package()\nwith
find_package(ament_cmake_auto REQUIRED)\n\nament_auto_package()\nEdit package.xml
We need to declare relevant dependencies in package.xml. For the above example we need to add the following content:
<buildtool_depend>rosidl_default_generators</buildtool_depend>\n\n<exec_depend>rosidl_default_runtime</exec_depend>\n\n<depend>geometry_msgs</depend>\n<depend>std_msgs</depend>\n\n<member_of_group>rosidl_interface_packages</member_of_group>\nWe need to replace <buildtool_depend>ament_cmake</buildtool_depend> with <buildtool_depend>ament_cmake_auto</buildtool_depend> in the package.xml file.
Build the custom message package
You can build the package in the root of your workspace, for example by running the following command:
colcon build --packages-select autoware_sensing_msgs\nNow the GnssInsOrientationStamped message will be discoverable by other packages in Autoware.
You can use the custom messages in Autoware by following these steps:
package.xml.<depend>autoware_sensing_msgs</depend>..hpp file of the relevant message in the code.#include <autoware_sensing_msgs/msg/gnss_ins_orientation_stamped.hpp>.This page shows some advanced and useful usage of colcon. If you need more detailed information, refer to the colcon documentation.
It is important that you always run colcon build from the workspace root because colcon builds only under the current directory. If you have mistakenly built in a wrong directory, run rm -rf build/ install/ log/ to clean the generated files.
colcon overlays workspaces if you have sourced the setup.bash of other workspaces before building a workspace. You should take care of this especially when you have multiple workspaces.
Run echo $COLCON_PREFIX_PATH to check whether workspaces are overlaid. If you find some workspaces are unnecessarily overlaid, remove all built files, restart the terminal to clean environment variables, and re-build the workspace.
For more details about workspace overlaying, refer to the ROS 2 documentation.
colcon sometimes causes errors of because of the old cache. To remove the cache and rebuild the workspace, run the following command:
rm -rf build/ install/\nIn case you know what packages to remove:
rm -rf {build,install}/{package_a,package_b}\nTo just build specified packages:
colcon build --packages-select <package_name1> <package_name2> ...\nTo build specified packages and their dependencies recursively:
colcon build --packages-up-to <package_name1> <package_name2> ...\nYou can also use these options for colcon test.
Set DCMAKE_BUILD_TYPE to change the optimization level.
Warning
If you specify DCMAKE_BUILD_TYPE=Debug or no DCMAKE_BUILD_TYPE is given for building the entire Autoware, it may be too slow to use.
colcon build --cmake-args -DCMAKE_BUILD_TYPE=Debug\ncolcon build --cmake-args -DCMAKE_BUILD_TYPE=RelWithDebInfo\ncolcon build --cmake-args -DCMAKE_BUILD_TYPE=Release\nCreate $COLCON_HOME/defaults.yaml to change the default configuration.
mkdir -p ~/.colcon\ncat << EOS > ~/.colcon/defaults.yaml\n{\n\"build\": {\n\"symlink-install\": true\n}\n}\nFor more details, see here.
"},{"location":"how-to-guides/others/advanced-usage-of-colcon/#generating-compile_commandsjson","title":"Generating compile_commands.json","text":"compile_commands.json is used by IDEs/tools to analyze the build dependencies and symbol relationships.
You can generate it with the flag DCMAKE_EXPORT_COMPILE_COMMANDS=1:
colcon build --cmake-args -DCMAKE_EXPORT_COMPILE_COMMANDS=1\nTo see the compiler and linker invocations for a package, use VERBOSE=1 and --event-handlers console_cohesion+:
VERBOSE=1 colcon build --packages-up-to <package_name> --event-handlers console_cohesion+\nFor other options, see here.
"},{"location":"how-to-guides/others/advanced-usage-of-colcon/#using-ccache","title":"Using Ccache","text":"Ccache can speed up recompilation. It is recommended to use it to save your time unless you have a specific reason not to do so.
Install Ccache:
sudo apt update && sudo apt install ccache\nWrite the following in your .bashrc:
export CC=\"/usr/lib/ccache/gcc\"\nexport CXX=\"/usr/lib/ccache/g++\"\nClang-Tidy is a powerful C++ linter.
"},{"location":"how-to-guides/others/applying-clang-tidy-to-ros-packages/#preparation","title":"Preparation","text":"You need to generate build/compile_commands.json before using Clang-Tidy.
colcon build --cmake-args -DCMAKE_EXPORT_COMPILE_COMMANDS=1\nclang-tidy -p build/ path/to/file1 path/to/file2 ...\nIf you want to apply Clang-Tidy to all files in a package, using the fd command is useful. To install fd, see the installation manual.
clang-tidy -p build/ $(fd -e cpp -e hpp --full-path \"/autoware_utils/\")\nRefer to the CLion Documentation.
"},{"location":"how-to-guides/others/applying-clang-tidy-to-ros-packages/#visual-studio-code","title":"Visual Studio Code","text":"Use either one of the following extensions:
If you encounter clang-diagnostic-error, try installing libomp-dev.
Related: https://github.com/autowarefoundation/autoware-github-actions/pull/172
"},{"location":"how-to-guides/others/debug-autoware/","title":"Debug Autoware","text":""},{"location":"how-to-guides/others/debug-autoware/#debug-autoware","title":"Debug Autoware","text":"This page provides some methods for debugging Autoware.
"},{"location":"how-to-guides/others/debug-autoware/#print-debug-messages","title":"Print debug messages","text":"The essential thing for debug is to print the program information clearly, which can quickly judge the program operation and locate the problem. Autoware uses ROS 2 logging tool to print debug messages, how to design console logging refer to tutorial Console logging.
"},{"location":"how-to-guides/others/debug-autoware/#using-ros-tools-debug-autoware","title":"Using ROS tools debug Autoware","text":""},{"location":"how-to-guides/others/debug-autoware/#using-command-line-tools","title":"Using command line tools","text":"ROS 2 includes a suite of command-line tools for introspecting a ROS 2 system. The main entry point for the tools is the command ros2, which itself has various sub-commands for introspecting and working with nodes, topics, services, and more. How to use the ROS 2 command line tool refer to tutorial CLI tools.
Rviz2 is a port of Rviz to ROS 2. It provides a graphical interface for users to view their robot, sensor data, maps, and more. You can run Rviz2 tool easily by:
rviz2\nWhen Autoware launch the simulators, the Rviz2 tool is opened by default to visualize the autopilot graphic information.
"},{"location":"how-to-guides/others/debug-autoware/#using-rqt-tools","title":"Using rqt tools","text":"RQt is a graphical user interface framework that implements various tools and interfaces in the form of plugins. You can run any RQt tools/plugins easily by:
rqt\nThis GUI allows you to choose any available plugins on your system. You can also run plugins in standalone windows. For example, RQt Console:
ros2 run rqt_console rqt_console\nrqt_graph: view node interaction
In complex applications, it may be helpful to get a visual representation of the ROS node interactions.
ros2 run rqt_graph rqt_graph\nrqt_console: view messages
rqt_console is a great gui for viewing ROS topics.
ros2 run rqt_console rqt_console\nrqt_plot: view data plots
rqt_plot is an easy way to plot ROS data in real time.
ros2 run rqt_plot rqt_plot\nros2_graph can be used to generate mermaid description of ROS 2 graphs to add on your markdown files.
It can also be used as a colorful alternative to rqt_graph even though it would require some tool to render the generated mermaid diagram.
It can be installed with:
pip install ros2-graph\nThen you can generate a mermaid description of the graph with:
ros2_graph your_node\n\n# or like with an output file\nros2_graph /turtlesim -o turtle_diagram.md\n\n# or multiple nodes\nros2_graph /turtlesim /teleop_turtle\nYou can then visualize these graphs with:
When your ROS 2 setup is not running as expected, you can check its settings with the ros2doctor tool.
ros2doctor checks all aspects of ROS 2, including platform, version, network, environment, running systems and more, and warns you about possible errors and reasons for issues.
It's as simple as just running ros2 doctor in your terminal.
It has the ability to list \"Subscribers without publishers\" for all topics in the system.
And this information can help you find if a necessary node isn't running.
For more details, see the following official documentation for Using ros2doctor to identify issues.
"},{"location":"how-to-guides/others/debug-autoware/#using-a-debugger-with-breakpoints","title":"Using a debugger with breakpoints","text":"Many IDE(e.g. Visual Studio Code, CLion) supports debugging C/C++ executable with GBD on linux platform. The following lists some references for using the debugger:
For any developers who wish to try and deploy Autoware as a microservices architecture, it is necessary to understand the software dependencies, communication, and implemented features of each ROS package/node.
As an example, the commands necessary to determine the dependencies for the Perception component are shown below.
"},{"location":"how-to-guides/others/determining-component-dependencies/#perception-component-dependencies","title":"Perception component dependencies","text":"To generate a graph of package dependencies, use the following colcon command:
colcon graph --dot --packages-up-to tier4_perception_launch | dot -Tpng -o graph.png\nTo generate a list of dependencies, use:
colcon list --packages-up-to tier4_perception_launch --names-only\nautoware_auto_geometry_msgs\nautoware_auto_mapping_msgs\nautoware_auto_perception_msgs\nautoware_auto_planning_msgs\nautoware_auto_vehicle_msgs\nautoware_cmake\nautoware_lint_common\nautoware_point_types\ncompare_map_segmentation\ndetected_object_feature_remover\ndetected_object_validation\ndetection_by_tracker\neuclidean_cluster\ngrid_map_cmake_helpers\ngrid_map_core\ngrid_map_cv\ngrid_map_msgs\ngrid_map_pcl\ngrid_map_ros\nground_segmentation\nimage_projection_based_fusion\nimage_transport_decompressor\ninterpolation\nkalman_filter\nlanelet2_extension\nlidar_apollo_instance_segmentation\nmap_based_prediction\nmulti_object_tracker\nmussp\nobject_merger\nobject_range_splitter\noccupancy_grid_map_outlier_filter\npointcloud_preprocessor\npointcloud_to_laserscan\nshape_estimation\ntensorrt_yolo\ntier4_autoware_utils\ntier4_debug_msgs\ntier4_pcl_extensions\ntier4_perception_launch\ntier4_perception_msgs\ntraffic_light_classifier\ntraffic_light_map_based_detector\ntraffic_light_ssd_fine_detector\ntraffic_light_visualization\nvehicle_info_util\nTip
To output a list of modules with their respective paths, run the command above without the --names-only parameter.
To see which ROS topics are being subscribed and published to, use rqt_graph as follows:
ros2 launch tier4_perception_launch perception.launch.xml mode:=lidar\nros2 run rqt_graph rqt_graph\nThis page will show you how to set up Eagleye in order to use it with Autoware. For the details of the integration proposal, please refer to this Discussion.
"},{"location":"how-to-guides/others/eagleye-integration-guide/#what-is-eagleye","title":"What is Eagleye?","text":"Eagleye is an open-source GNSS/IMU-based localizer initially developed by MAP IV. Inc. It provides a cost-effective alternative to LiDAR and point cloud-based localization by using low-cost GNSS and IMU sensors to provide vehicle position, orientation, and altitude information. By integrating Eagleye into Autoware, users can choose between LiDAR and point cloud-based localization stacks or GNSS/IMU-based Eagleye localizer, depending on their specific needs and operating environment.
"},{"location":"how-to-guides/others/eagleye-integration-guide/#architecture","title":"Architecture","text":"Eagleye can be utilized in the Autoware localization stack in two ways:
Feed only twist into the EKF localizer.
Feed both twist and pose from Eagleye into the EKF localizer (twist can also be used with regular gyro_odometry).
Note that RTK positioning is only required for localization using the Eagleye pose. RTK positioning is not required for twist.
"},{"location":"how-to-guides/others/eagleye-integration-guide/#requirements","title":"Requirements","text":"GNSS/IMU/vehicle speed is required for Eagleye input.
"},{"location":"how-to-guides/others/eagleye-integration-guide/#imu-topic","title":"IMU topic","text":"sensor_msgs/msg/Imu are supported for IMU.
geometry_msgs/msg/TwistStamped and geometry_msgs/msg/TwistWithCovarianceStamped are supported for the input vehicle speed.
Eagleye requires latitude/longitude height information and velocity information generated by the GNSS receiver. Your GNSS ROS driver must publish the following messages:
sensor_msgs/msg/NavSatFix: This message contains latitude, longitude, and height information.geometry_msgs/msg/TwistWithCovarianceStamped: This message contains gnss doppler velocity information.Eagleye has been tested with the following example GNSS ROS drivers: ublox_gps and septentrio_gnss_driver. The settings needed for each of these drivers are as follows:
sensor_msgs/msg/NavSatFix and geometry_msgs/msg/TwistWithCovarianceStamped required by Eagleye with default settings. Therefore, no additional settings are required.publish.navsatfix and publish.twist in the config file gnss.yaml to trueClone the following three packages for Eagleye:
You need to install Eagleye-related packages and change Autoware's launcher. Four files are required in the Autoware localization launcher to run Eagleye: eagleye_rt.launch.xml, eagleye_config.yaml, gnss_converter.launch.xml, and fix2pose.launch.xml.
You must correctly specify input topics for GNSS latitude, longitude, and height information, GNSS speed information, IMU information, and vehicle speed information in the eagleye_config.yaml.
# Topic\ntwist:\ntwist_type: 1 # TwistStamped : 0, TwistWithCovarianceStamped: 1\ntwist_topic: /sensing/vehicle_velocity_converter/twist_with_covariance\nimu_topic: /sensing/imu/tamagawa/imu_raw\ngnss:\nvelocity_source_type: 2 # rtklib_msgs/RtklibNav: 0, nmea_msgs/Sentence: 1, ublox_msgs/NavPVT: 2, geometry_msgs/TwistWithCovarianceStamped: 3\nvelocity_source_topic: /sensing/gnss/ublox/navpvt\nllh_source_type: 2 # rtklib_msgs/RtklibNav: 0, nmea_msgs/Sentence: 1, sensor_msgs/NavSatFix: 2\nllh_source_topic: /sensing/gnss/ublox/nav_sat_fix\nAlso, the frequency of GNSS and IMU must be set in eagleye_config.yaml
common:\nimu_rate: 50\ngnss_rate: 5\nThe basic parameters that do not need to be changed except those mentioned above, i.e., topic names and sensors' frequency, are described below here. Additionally, the parameters for converting sensor_msgs/msg/NavSatFix to geometry_msgs/msg/PoseWithCovarianceStamped is listed in fix2pose.yaml.
Please refer to map4_localization_launch in the autoware.universe package and map4_localization_component.launch.xml in autoware_launch package for information on how to modify the localization launch.
Eagleye has a function for position estimation and a function for twist estimation, namely pose_estimator and twist_estimator, respectively.
tier4_localization_launch gyro_odometry ndt_scan_matcher map4_localization_launch/eagleye_twist_localization_launch eagleye_rt(gyro/odom/gnss fusion) ndt_scan_matcher map4_localization_launch/eagleye_pose_twist_localization_launch eagleye_rt(gyro/odom/gnss fusion) eagleye_rt(gyro/odom/gnss fusion) In Autoware, you can set the pose estimator to GNSS by setting pose_estimator_mode:=gnss in map4_localization_component.launch.xml in autoware_launch package. Note that the output position might not appear to be in the point cloud maps if you are using maps that are not properly georeferenced. In the case of a single GNSS antenna, initial position estimation (dynamic initialization) can take several seconds to complete after starting to run in an environment where GNSS positioning is available.
Alternatively, the twist estimator can be set to Eagleye and the pose estimator to NDT by specifying pose_estimator_mode:=lidar in the same launch file. Unlike Eagleye position estimation, Eagleye twist estimation first outputs uncorrected raw values when activated, and then outputs corrected twists as soon as static initialization is complete.
Enable Eagleye in Autoware by switching the localization module in autoware.launch.xml and the pose_estimator_mode parameter in map4_localization_component.launch.xml in autoware.launch.xml.
When using Eagleye, comment out tier4_localization_component.launch.xml and start map4_localization_component.launch.xml in autoware.launch.xml.
<!-- Localization -->\n<group if=\"$(var launch_localization)\">\n<!-- <include file=\"$(find-pkg-share autoware_launch)/launch/components/tier4_localization_component.launch.xml\"/> -->\n<include file=\"$(find-pkg-share autoware_launch)/launch/components/map4_localization_component.launch.xml\"/>\n</group>\nEagleye requires an initialization process for proper operation. Without initialization, the output for twist will be in the raw value, and the pose data will not be available.
The first step is static initialization, which involves allowing the Eagleye to remain stationary for approximately 5 seconds after startup to estimate the yaw-rate offset.
The next step is dynamic initialization, which involves running the Eagleye in a straight line for approximately 30 seconds. This process estimates the scale factor of wheel speed and azimuth angle. Once dynamic initialization is complete, the Eagleye will be able to provide corrected twist and pose data.
"},{"location":"how-to-guides/others/fixing-dependent-package-versions/","title":"Fixing dependent package versions","text":""},{"location":"how-to-guides/others/fixing-dependent-package-versions/#fixing-dependent-package-versions","title":"Fixing dependent package versions","text":"Autoware manages dependent package versions in autoware.repos. For example, let's say you make a branch in autoware.universe and add new features. Suppose you update other dependencies with vcs pull after cutting a branch from autoware.universe. Then the version of autoware.universe you are developing and other dependencies will become inconsistent, and the entire Autoware build will fail. We recommend saving the dependent package versions by executing the following command when starting the development.
vcs export src --exact > my_autoware.repos\nAlthough CUDA installation is recommended to achieve better performance for object detection and traffic light recognition in Autoware Universe, it is possible to run these algorithms without CUDA. The following subsections briefly explain how to run each algorithm in such an environment.
"},{"location":"how-to-guides/others/running-autoware-without-cuda/#running-2d3d-object-detection-without-cuda","title":"Running 2D/3D object detection without CUDA","text":"Autoware Universe's object detection can be run using one of five possible configurations:
lidar_centerpointlidar_apollo_instance_segmentationlidar-apollo + tensorrt_yololidar-centerpoint + tensorrt_yoloeuclidean_clusterOf these five configurations, only the last one (euclidean_cluster) can be run without CUDA. For more details, refer to the euclidean_cluster module's README file.
For traffic light recognition (both detection and classification), there are two modules that require CUDA:
traffic_light_ssd_fine_detectortraffic_light_classifierTo run traffic light detection without CUDA, set enable_fine_detection to false in the traffic light launch file. Doing so disables the traffic_light_ssd_fine_detector such that traffic light detection is handled by the map_based_traffic_light_detector module instead.
To run traffic light classification without CUDA, set use_gpu to false in the traffic light classifier launch file. Doing so will force the traffic_light_classifier to use a different classification algorithm that does not require CUDA or a GPU.
Autoware targets the platforms listed below. It may change in future versions of Autoware.
The Autoware Foundation provides no support on other platforms than those listed below.
"},{"location":"installation/#architecture","title":"Architecture","text":"Info
Autoware is scalable and can be customized to work with distributed or less powerful hardware. The minimum hardware requirements given below are just a general recommendation. However, performance will be improved with more cores, RAM and a higher-spec graphics card or GPU core.
Although GPU is not required to run basic functionality, it is mandatory to enable the following neural network related functions:
For details of how to enable object detection and traffic light detection/classification without a GPU, refer to the Running Autoware without CUDA.
"},{"location":"installation/#installing-autoware","title":"Installing Autoware","text":"There are two ways to set up Autoware. Choose one according to your preference.
If any issues occur during installation, refer to the Support page.
"},{"location":"installation/#1-docker-installation","title":"1. Docker installation","text":"Docker can ensure that all developers in a project have a common, consistent development environment. It is recommended for beginners, casual users, people who are unfamiliar with Ubuntu.
For more information, refer to the Docker installation guide.
"},{"location":"installation/#2-source-installation","title":"2. Source installation","text":"Source installation is for the cases where more granular control of the installation environment is needed. It is recommended for experienced users or people who want to customize their environment. Note that some problems may occur depending on your local environment.
For more information, refer to the source installation guide.
"},{"location":"installation/#installing-related-tools","title":"Installing related tools","text":"Some other tools are required depending on the evaluation you want to do. For example, to run an end-to-end simulation you need to install an appropriate simulator.
For more information, see here.
"},{"location":"installation/#additional-settings-for-developers","title":"Additional settings for developers","text":"There are also tools and settings for developers, such as Shells or IDEs.
For more information, see here.
"},{"location":"installation/additional-settings-for-developers/","title":"Additional settings for developers","text":""},{"location":"installation/additional-settings-for-developers/#additional-settings-for-developers","title":"Additional settings for developers","text":""},{"location":"installation/additional-settings-for-developers/#console-settings-for-ros-2","title":"Console settings for ROS 2","text":""},{"location":"installation/additional-settings-for-developers/#colorizing-logger-output","title":"Colorizing logger output","text":"By default, ROS 2 logger doesn't colorize the output. To colorize it, write the following in your .bashrc:
export RCUTILS_COLORIZED_OUTPUT=1\nBy default, ROS 2 logger doesn't output detailed information such as file name, function name, or line number. To customize it, write the following in your .bashrc:
export RCUTILS_CONSOLE_OUTPUT_FORMAT=\"[{severity} {time}] [{name}]: {message} ({function_name}() at {file_name}:{line_number})\"\nFor more options, see here.
"},{"location":"installation/additional-settings-for-developers/#network-settings-for-ros-2","title":"Network settings for ROS 2","text":"ROS 2 employs DDS, and the configuration of ROS 2 and DDS is described separately. For ROS 2 networking concepts, refer to the official documentation.
"},{"location":"installation/additional-settings-for-developers/#ros-2-network-setting","title":"ROS 2 network setting","text":"ROS 2 multicasts data on the local network by default. Therefore, when you develop in an office, the data flows over the local network of your office. It may cause collisions of packets or increases in network traffic.
To avoid these, there are two options.
Unless you plan to use multiple host computers on the local network, localhost-only communication is recommended. For details, refer to the sections below.
"},{"location":"installation/additional-settings-for-developers/#enabling-localhost-only-communication","title":"Enabling localhost-only communication","text":"Write the following in your .bashrc: For more information, see the ROS 2 documentation.
export ROS_LOCALHOST_ONLY=1\nIf you export ROS_LOCALHOST_ONLY=1, MULTICAST must be enabled at the loopback address. To verify that MULTICAST is enabled, use the following command.
$ ip link show lo\n1: lo: <LOOPBACK,MULTICAST,UP,LOWER_UP> mtu 65536 qdisc noqueue state UNKNOWN mode DEFAULT group default qlen 1000\nIf the word MULTICAST is not printed, use the following command to enable it.
sudo ip link set lo multicast on\nROS 2 uses ROS_DOMAIN_ID to create groups and communicate between machines in the groups. Since all ROS 2 nodes use domain ID 0 by default, it may cause unintended interference.
To avoid it, set a different domain ID for each group in your .bashrc:
# Replace X with the Domain ID you want to use\n# Domain ID should be a number in range [0, 101] (inclusive)\nexport ROS_DOMAIN_ID=X\nAlso confirm that ROS_LOCALHOST_ONLY is 0 by using the following command.
echo $ROS_LOCALHOST_ONLY # If the output is 1, localhost has priority.\nFor more information, see the ROS 2 Documentation.
"},{"location":"installation/additional-settings-for-developers/#dds-settings","title":"DDS settings","text":"Autoware uses DDS for inter-node communication. ROS 2 documentation recommends users to tune DDS to utilize its capability. Especially, receive buffer size is the critical parameter for Autoware. If the parameter is not large enough, Autoware will failed in receiving large data like point cloud or image.
"},{"location":"installation/additional-settings-for-developers/#tuning-dds","title":"Tuning DDS","text":"Unless customized, CycloneDDS is adopted by default. For example, to execute Autoware with CycloneDDS, prepare a config file. A sample config file is given below. Save it as cyclonedds_config.xml.
<?xml version=\"1.0\" encoding=\"UTF-8\" ?>\n<CycloneDDS xmlns=\"https://cdds.io/config\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:schemaLocation=\"https://cdds.io/config https://raw.githubusercontent.com/eclipse-cyclonedds/cyclonedds/master/etc/cyclonedds.xsd\">\n<Domain Id=\"any\">\n<General>\n<Interfaces>\n<NetworkInterface autodetermine=\"true\" priority=\"default\" multicast=\"default\" />\n</Interfaces>\n<AllowMulticast>default</AllowMulticast>\n<MaxMessageSize>65500B</MaxMessageSize>\n</General>\n<Internal>\n<SocketReceiveBufferSize min=\"10MB\"/>\n<Watermarks>\n<WhcHigh>500kB</WhcHigh>\n</Watermarks>\n</Internal>\n</Domain>\n</CycloneDDS>\nThis configuration is mostly taken from Eclipse Cyclone DDS:Run-time configuration documentation. You can see why each value is set as such under the documentation link.
Set the config file path and enlarge the Linux kernel maximum buffer size before launching Autoware.
export CYCLONEDDS_URI=file:///absolute/path/to/cyclonedds_config.xml\nsudo sysctl -w net.core.rmem_max=2147483647\nFor more information, Refer to ROS 2 documentation. Reading user guide for chosen DDS is helpful for more understanding.
"},{"location":"installation/additional-settings-for-developers/#tuning-dds-for-multiple-host-computers-for-advanced-users","title":"Tuning DDS for multiple host computers (for advanced users)","text":"When Autoware runs on multiple host computers, IP Fragmentation should be taken into account. As ROS 2 documentation recommends, parameters for IP Fragmentation should be set as shown in the following example.
sudo sysctl -w net.ipv4.ipfrag_time=3\nsudo sysctl -w net.ipv4.ipfrag_high_thresh=134217728 # (128 MB)\nClone autowarefoundation/autoware and move to the directory.
git clone https://github.com/autowarefoundation/autoware.git\ncd autoware\nYou can install the dependencies either manually or using the provided Ansible script.
Note: Before installing NVIDIA libraries, confirm and agree with the licenses.
Be very careful with this method. Make sure you read and confirmed all the steps in the Ansible configuration before using it.
If you've manually installed the dependencies, you can skip this section.
./setup-dev-env.sh docker\nYou might need to log out and log back to make the current user able to use docker.
"},{"location":"installation/autoware/docker-installation-devel/#how-to-set-up-a-workspace","title":"How to set up a workspace","text":"Warning
Before proceeding, confirm and agree with the NVIDIA Deep Learning Container license. By pulling and using the Autoware Universe images, you accept the terms and conditions of the license.
Create the autoware_map directory for map data later.
mkdir ~/autoware_map\nPull the Docker image
docker pull ghcr.io/autowarefoundation/autoware-universe:latest-cuda\nLaunch a Docker container.
For amd64 architecture computers with NVIDIA GPU:
rocker --nvidia --x11 --user --volume $HOME/autoware --volume $HOME/autoware_map -- ghcr.io/autowarefoundation/autoware-universe:latest-cuda\nIf you want to run container without using NVIDIA GPU, or for arm64 architecture computers:
rocker -e LIBGL_ALWAYS_SOFTWARE=1 --x11 --user --volume $HOME/autoware --volume $HOME/autoware_map -- ghcr.io/autowarefoundation/autoware-universe:latest-cuda\nFor detailed reason could be found here
For more advanced usage, see here.
After that, move to the workspace in the container:
cd autoware\nCreate the src directory and clone repositories into it.
mkdir src\nvcs import src < autoware.repos\nUpdate dependent ROS packages.
The dependency of Autoware may change after the Docker image was created. In that case, you need to run the following commands to update the dependency.
sudo apt update\nrosdep update\nrosdep install -y --from-paths src --ignore-src --rosdistro $ROS_DISTRO\nBuild the workspace.
colcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release\nIf there is any build issue, refer to Troubleshooting.
Update the Docker image.
docker pull ghcr.io/autowarefoundation/autoware-universe:latest-cuda\nLaunch a Docker container.
For amd64 architecture computers:
rocker --nvidia --x11 --user --volume $HOME/autoware -- ghcr.io/autowarefoundation/autoware-universe:latest-cuda\nIf you want to run container without using NVIDIA GPU, or for arm64 architecture computers:
rocker -e LIBGL_ALWAYS_SOFTWARE=1 --x11 --user --volume $HOME/autoware -- ghcr.io/autowarefoundation/autoware-universe:latest-cuda\nUpdate the .repos file.
cd autoware\ngit pull\nUpdate the repositories.
vcs import src < autoware.repos\nvcs pull src\nBuild the workspace.
colcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release\nInstalling dependencies manually
Create the autoware_map directory for map data later.
mkdir ~/autoware_map\nLaunch a Docker container.
rocker --nvidia --x11 --user --volume $HOME/autoware_map -- ghcr.io/autowarefoundation/autoware-universe:humble-latest-prebuilt\nFor more advanced usage, see here.
Run Autoware simulator
Inside the container, you can run the Autoware simulation by following this tutorial:
planning simulation
rosbag replay simulation.
Info
Since this page explains Docker-specific information, it is recommended to see Source installation as well if you need detailed information.
Here are two ways to install Autoware by docker:
prebuilt image, this is a quick start, this way you can only run Autoware simulator and not develop Autoware, it is only suitable for beginnersdevel image, which supports developing and running Autoware using dockerdocker installation for quick start
"},{"location":"installation/autoware/docker-installation/#docker-installation-for-development","title":"Docker installation for development","text":"docker installation for development
"},{"location":"installation/autoware/docker-installation/#troubleshooting","title":"Troubleshooting","text":"Here are solutions for a few specific errors:
"},{"location":"installation/autoware/docker-installation/#cuda-error-forward-compatibility-was-attempted-on-non-supported-hw","title":"cuda error: forward compatibility was attempted on non supported hw","text":"When starting Docker with GPU support enabled for NVIDIA graphics, you may sometimes receive the following error:
docker: Error response from daemon: OCI runtime create failed: container_linux.go:349: starting container process caused \"process_linux.go:449: container init caused \\\"process_linux.go:432: running prestart hook 0 caused \\\\\\\"error running hook: exit status 1, stdout: , stderr: nvidia-container-cli: initialization error: cuda error: forward compatibility was attempted on non supported hw\\\\\\\\n\\\\\\\"\\\"\": unknown.\nERROR: Command return non-zero exit code (see above): 125\nThis usually indicates that a new NVIDIA graphics driver has been installed (usually via apt) but the system has not yet been restarted. A similar message may appear if the graphics driver is not available, for example because of resuming after suspend.
To fix this, restart your system after installing the new NVIDIA driver.
"},{"location":"installation/autoware/docker-installation/#docker-with-nvidia-gpu-fails-to-start-autoware-on-arm64-devices","title":"Docker with NVIDIA gpu fails to start Autoware on arm64 devices","text":"When starting Docker with GPU support enabled for NVIDIA graphics on arm64 devices, e.g. NVIDIA jetson AGX xavier, you may receive the following error:
nvidia@xavier:~$ rocker --nvidia --x11 --user --volume $HOME/autoware -- ghcr.io/autowarefoundation/autoware-universe:humble-latest-cuda-arm64\n...\n\nCollecting staticx==0.12.3\nDownloading https://files.pythonhosted.org/packages/92/ff/d9960ea1f9db48d6044a24ee0f3d78d07bcaddf96eb0c0e8806f941fb7d3/staticx-0.12.3.tar.gz (68kB)\nComplete output from command python setup.py egg_info:\nTraceback (most recent call last):\nFile \"\", line 1, in\nFile \"/tmp/pip-install-m_nm8mya/staticx/setup.py\", line 4, in\nfrom wheel.bdist_wheel import bdist_wheel\nModuleNotFoundError: No module named 'wheel'\n\nCommand \"python setup.py egg_info\" failed with error code 1 in /tmp/pip-install-m_nm8mya/staticx/\n...\nThis error exists in current version of rocker tool, which relates to the os_detection function of rocker.
To fix this error, temporary modification of rocker source code is required, which is not recommended.
At current stage, it is recommended to run docker without NVIDIA gpu enabled for arm64 devices:
rocker -e LIBGL_ALWAYS_SOFTWARE=1 --x11 --user --volume $HOME/autoware -- ghcr.io/autowarefoundation/autoware-universe:latest-cuda\nThis tutorial will be updated after official fix from rocker.
"},{"location":"installation/autoware/docker-installation/#tips","title":"Tips","text":""},{"location":"installation/autoware/docker-installation/#non-native-arm64-system","title":"Non-native arm64 System","text":"This section describes a process to run arm64 systems on amd64 systems using qemu-user-static.
Initially, your system is usually incompatible with arm64 systems. To check that:
$ docker run --rm -t arm64v8/ubuntu uname -m\nWARNING: The requested image's platform (linux/arm64/v8) does not match the detected host platform (linux/amd64) and no specific platform was requested\nstandard_init_linux.go:228: exec user process caused: exec format error\nInstalling qemu-user-static enables us to run arm64 images on amd64 systems.
$ sudo apt-get install qemu-user-static\n$ docker run --rm --privileged multiarch/qemu-user-static --reset -p yes\n$ docker run --rm -t arm64v8/ubuntu uname -m\nWARNING: The requested image's platform (linux/arm64/v8) does not match the detected host platform (linux/amd64) and no specific platform was requested\naarch64\nTo run Autoware's Docker images of arm64 architecture, add the suffix -arm64.
$ docker run --rm -it ghcr.io/autowarefoundation/autoware-universe:humble-latest-cuda-arm64\nWARNING: The requested image's platform (linux/arm64) does not match the detected host platform (linux/amd64) and no specific platform was requested\nroot@5b71391ad50f:/autoware#\nOS
ROS
For ROS 2 system dependencies, refer to REP-2000.
sudo apt-get -y update\nsudo apt-get -y install git\nNote: If you wish to use ROS 2 Galactic on Ubuntu 20.04, refer to installation instruction from galactic branch, but be aware that Galactic version of Autoware might not have latest features.
"},{"location":"installation/autoware/source-installation/#how-to-set-up-a-development-environment","title":"How to set up a development environment","text":"Clone autowarefoundation/autoware and move to the directory.
git clone https://github.com/autowarefoundation/autoware.git\ncd autoware\nIf you are installing Autoware for the first time, you can automatically install the dependencies by using the provided Ansible script.
./setup-dev-env.sh\nIf you encounter any build issues, please consult the Troubleshooting section for assistance.
Info
Before installing NVIDIA libraries, please ensure that you have reviewed and agreed to the licenses.
Note
The following items will be automatically installed. If the ansible script doesn't work or if you already have different versions of dependent libraries installed, please install the following items manually.
Create the src directory and clone repositories into it.
Autoware uses vcstool to construct workspaces.
cd autoware\nmkdir src\nvcs import src < autoware.repos\nInstall dependent ROS packages.
Autoware requires some ROS 2 packages in addition to the core components. The tool rosdep allows an automatic search and installation of such dependencies. You might need to run rosdep update before rosdep install.
source /opt/ros/humble/setup.bash\nrosdep install -y --from-paths src --ignore-src --rosdistro $ROS_DISTRO\nBuild the workspace.
Autoware uses colcon to build workspaces. For more advanced options, refer to the documentation.
colcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release\nIf there is any build issue, refer to Troubleshooting.
Update the .repos file.
cd autoware\ngit pull\nUpdate the repositories.
vcs import src < autoware.repos\nvcs pull src\nFor Git users:
vcs import is similar to git checkout.vcs pull is similar to git pull.For more information, refer to the official documentation.
Install dependent ROS packages.
source /opt/ros/humble/setup.bash\nrosdep install -y --from-paths src --ignore-src --rosdistro $ROS_DISTRO\nBuild the workspace.
colcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release\nWarning
Under Construction
"},{"location":"support/","title":"Support","text":""},{"location":"support/#support","title":"Support","text":"This page explains several support resources.
This page explains several documentation sites that are useful for Autoware and ROS development.
This page explains the support mechanisms we provide.
Warning
Before asking for help, search and read this documentation site carefully. Also, follow the discussion guidelines for discussions.
Choose appropriate resources depending on what kind of help you need and read the detailed description in the sections below.
Docs guide shows the list of useful documentation sites. Visit them and see if there is any information related to your problem.
Note that the documentation sites aren't always up-to-date and perfect. If you find out that some information is wrong, unclear, or missing in Autoware docs, feel free to submit a pull request following the contribution guidelines.
Warning
Since this documentation site is still under construction, there are some empty pages.
"},{"location":"support/support-guidelines/#github-discussions","title":"GitHub Discussions","text":"If you encounter a problem with Autoware, check existing issues and questions and search for similar issues first.
Issues
Note that Autoware has multiple repositories listed in autoware.repos. It is recommended to search across the repositories.
If no answer was found, create a new question thread here. If your question is not answered within a week, then @mention the maintainers to remind them.
Also, there are other discussion types such as feature requests or design discussions. Feel free to open or join such discussions.
If you don't know how to create a discussion, refer to GitHub Docs.
"},{"location":"support/support-guidelines/#github-issues","title":"GitHub Issues","text":"If you have a problem and you have confirmed it is a bug, find the appropriate repository and create a new issue there. If you can't determine the appropriate repository, ask the maintainers for help by creating a new discussion in the Q&A category.
Warning
Do not create issues for questions or unconfirmed bugs. If such issues are created, maintainers will transfer them to GitHub Discussions.
If you want to fix the bug by yourself, discuss the approach with maintainers and submit a pull request.
"},{"location":"support/support-guidelines/#discord","title":"Discord","text":"Autoware has a Discord server for casual communication between contributors.
The Autoware Discord server is a good place for the following activities:
Note that it is not the right place to get help for your issues.
"},{"location":"support/support-guidelines/#ros-discourse","title":"ROS Discourse","text":"If you want to widely discuss a topic with the general Autoware and ROS community or ask a question not related to Autoware's bugs, post to the Autoware category on ROS Discourse.
Warning
Do not post questions about bugs to ROS Discourse!
"},{"location":"support/troubleshooting/","title":"Troubleshooting","text":""},{"location":"support/troubleshooting/#troubleshooting","title":"Troubleshooting","text":""},{"location":"support/troubleshooting/#setup-issues","title":"Setup issues","text":""},{"location":"support/troubleshooting/#cuda-related-errors","title":"CUDA-related errors","text":"When installing CUDA, errors may occur because of version conflicts. To resolve these types of errors, try one of the following methods:
Unhold all CUDA-related libraries and rerun the setup script.
sudo apt-mark unhold \\\n\"cuda*\" \\\n\"libcudnn*\" \\\n\"libnvinfer*\" \\\n\"libnvonnxparsers*\" \\\n\"libnvparsers*\" \\\n\"tensorrt*\" \\\n\"nvidia*\"\n\n./setup-dev-env.sh\nUninstall all CUDA-related libraries and rerun the setup script.
sudo apt purge \\\n\"cuda*\" \\\n\"libcudnn*\" \\\n\"libnvinfer*\" \\\n\"libnvonnxparsers*\" \\\n\"libnvparsers*\" \\\n\"tensorrt*\" \\\n\"nvidia*\"\n\nsudo apt autoremove\n\n./setup-dev-env.sh\nWarning
Note that this may break your system and run carefully.
Run the setup script without installing CUDA-related libraries.
./setup-dev-env.sh --no-nvidia\nWarning
Note that some components in Autoware Universe require CUDA, and only the CUDA version in the env file is supported at this time. Autoware may work with other CUDA versions, but those versions are not supported and functionality is not guaranteed.
"},{"location":"support/troubleshooting/#build-issues","title":"Build issues","text":""},{"location":"support/troubleshooting/#insufficient-memory","title":"Insufficient memory","text":"Building Autoware requires a lot of memory, and your machine can freeze or crash if memory runs out during a build. To avoid this problem, 16-32GB of swap should be configured.
# Optional: Check the current swapfile\nfree -h\n\n# Remove the current swapfile\nsudo swapoff /swapfile\nsudo rm /swapfile\n\n# Create a new swapfile\nsudo fallocate -l 32G /swapfile\nsudo chmod 600 /swapfile\nsudo mkswap /swapfile\nsudo swapon /swapfile\n\n# Optional: Check if the change is reflected\nfree -h\nFor more detailed configuration steps, along with an explanation of swap, refer to Digital Ocean's \"How To Add Swap Space on Ubuntu 20.04\" tutorial
If there are too many CPU cores (more than 64) in your machine, it might requires larger memory. A workaround here is to limit the job number while building.
MAKEFLAGS=\"-j4\" colcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release\nYou can adjust -j4 to any number based on your system. For more details, see the manual page of GNU make.
By reducing the number of packages built in parallel, you can also reduce the amount of memory used. In the following example, the number of packages built in parallel is set to 1, and the number of jobs used by make is limited to 1.
MAKEFLAGS=\"-j1\" colcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release --parallel-workers 1\nNote
By lowering both the number of packages built in parallel and the number of jobs used by make, you can reduce the memory usage. However, this also means that the build process takes longer.
If you are working with the latest version of Autoware, issues can occur due to out-of-date software or old build files.
To resolve these types of problems, first try cleaning your build artifacts and rebuilding:
rm -rf build/ install/ log/\ncolcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release\nIf the error is not resolved, remove src/ and update your workspace according to installation type (Docker / source).
Warning
Before removing src/, confirm that there are no modifications in your local environment that you want to keep!
If errors still persist after trying the steps above, delete the entire workspace, clone the repository once again and restart the installation process.
rm -rf autoware/\ngit clone https://github.com/autowarefoundation/autoware.git\nIn principle, errors should not occur when using a fixed version. That said, possible causes include:
.bashrc file, environment variables, and library versions.In addition to the causes listed above, there are two common misunderstandings around the use of fixed versions.
You used a fixed version for autowarefoundation/autoware only. All of the repository versions in the .repos file must be specified in order to use a completely fixed version.
You didn't update the workspace after changing the branch of autowarefoundation/autoware. Changing the branch of autowarefoundation/autoware does not affect the files under src/. You have to run the vcs import command to update them.
During building the following issue can occurs
pkg_resources.extern.packaging.version.InvalidVersion: Invalid version: '0.23ubuntu1'\nThe error is due to the fact that for versions between 66.0.0 and 67.5.0 setuptools enforces the python packages to be PEP-440 conformant. Since version 67.5.1 setuptools has a fallback that makes it possible to work with old packages again.
The solution is to update setuptools to the newest version with the following command
pip install --upgrade setuptools\nIf any errors occur when running Autoware with Docker or rocker, first confirm that your Docker installation is working correctly by running the following commands:
docker run --rm -it hello-world\ndocker run --rm -it ubuntu:latest\nNext, confirm that you are able to access the base Autoware image that is stored on the GitHub Packages website
docker run --rm -it ghcr.io/autowarefoundation/autoware-universe:latest\nSymptoms:
If you have any of these symptoms, please the Performance Troubleshooting page.
"},{"location":"support/troubleshooting/#map-does-not-display-when-running-the-planning-simulator","title":"Map does not display when running the Planning Simulator","text":"When running the Planning Simulator, the most common reason for the map not being displayed in RViz is because the map path has not been specified correctly in the launch command. You can confirm if this is the case by searching for Could not find lanelet map under {path-to-map-dir}/lanelet2_map.osm errors in the log.
Another possible reason is that map loading is taking a long time due to poor DDS performance. For this, please visit the Performance Troubleshooting page.
"},{"location":"support/troubleshooting/performance-troubleshooting/","title":"Performance Troubleshooting","text":""},{"location":"support/troubleshooting/performance-troubleshooting/#performance-troubleshooting","title":"Performance Troubleshooting","text":"Overall symptoms:
Make sure that the multicast is enabled for your interface.
For example when you run following:
source /opt/ros/humble/setup.bash\nros2 run demo_nodes_cpp talker\nIf you get the error message selected interface \"{your-interface-name}\" is not multicast-capable: disabling multicast, this should be fixed.
Run the following command to allow multicast:
sudo ip link set multicast on {your-interface-name}\nThis way DDS will function as intended and multiple subscribers can receive data from a single publisher without any significant degradation in performance.
This is a temporary solution. And will be reverted once the computer restarts.
To make it permanent either,
OR put following lines to the ~/.bashrc file:
if [ ! -e /tmp/multicast_is_set ]; then\nsudo ip link set lo multicast on\ntouch /tmp/multicast_is_set\nfi\nCheck the ~/.bash_history file to see if there are any colcon build directives without -DCMAKE_BUILD_TYPE=Release or -DCMAKE_BUILD_TYPE=RelWithDebInfo flags at all.
Even if a build starts with these flags but same workspace gets compiled without these flags, it will still be a slow build in the end.
In addition, the nodes will run slow in general, especially the pointcloud_preprocessor nodes.
Example issue: issue2597
"},{"location":"support/troubleshooting/performance-troubleshooting/#solution_1","title":"Solution","text":"build, install and optionally log folders in the main autoware folder.Compile the Autoware with either Release or RelWithDebInfo tags:
colcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release\n# Or build with debug flags too (comparable performance but you can debug too)\ncolcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=RelWithDebInfo\nRun following to check the middleware used:
echo $RMW_IMPLEMENTATION\nThe return line should be rmw_cyclonedds_cpp. If not, apply the solution.
If you are using a different DDS middleware, we might not have official support for it just yet.
"},{"location":"support/troubleshooting/performance-troubleshooting/#solution_2","title":"Solution","text":"Add export RMW_IMPLEMENTATION=rmw_cyclonedds_cpp as a separate line in you ~/.bashrc file.
Run following to check the configuration .xml file of the CycloneDDS:
echo $CYCLONEDDS_URI\nThe return line should be a valid path pointing to an .xml file with CycloneDDS configuration.
Also check if the file is configured correctly:
cat !{echo $CYCLONEDDS_URI}\nThis should print the .xml file on the terminal.
Follow DDS settings:Tuning DDS documentation and make sure:
export CYCLONEDDS_URI=/absolute_path_to_your/cyclonedds_config.xml as a line on your ~/.bashrc file.cyclonedds_config.xml with the configuration provided in the documentation.sysctl net.core.rmem_max, it should return at least net.core.rmem_max = 2147483647.sysctl net.ipv4.ipfrag_time, it should return around: net.ipv4.ipfrag_time = 3sysctl net.ipv4.ipfrag_high_thresh, it should return at around: net.ipv4.ipfrag_high_thresh = 134217728More info on these values: Cross-vendor tuning
"},{"location":"support/troubleshooting/performance-troubleshooting/#solution_4","title":"Solution","text":"Either:
Create the following file: sudo touch /etc/sysctl.d/10-cyclone-max.conf (recommended)
Edit the file to contain (sudo gedit /etc/sysctl.d/10-cyclone-max.conf):
net.core.rmem_max=2147483647\nnet.ipv4.ipfrag_time=3\nnet.ipv4.ipfrag_high_thresh=134217728 # (128 MB)\nEither restart the computer or run following to enable the changes:
sudo sysctl -w net.core.rmem_max=2147483647\nsudo sysctl -w net.ipv4.ipfrag_time=3\nsudo sysctl -w net.ipv4.ipfrag_high_thresh=134217728\nOR put following lines to the ~/.bashrc file:
if [ ! -e /tmp/kernel_network_conf_is_set ]; then\nsudo sysctl -w net.core.rmem_max=2147483647\nsudo sysctl -w net.ipv4.ipfrag_time=3\nsudo sysctl -w net.ipv4.ipfrag_high_thresh=134217728 # (128 MB)\nfi\nRun following to check it:
echo $ROS_LOCALHOST_ONLY\nThe return line should be 1. If not, apply the solution.
export $ROS_LOCALHOST_ONLY=1 as a separate line in you ~/.bashrc file.loopback network interface (i.e., localhost) for communication, rather than using the network interface card (NIC) for Ethernet or Wi-Fi. This can reduce network traffic and potential conflicts with other devices on the network, resulting in better performance and stability.Simulations provide a way of verifying Autoware's functionality before field testing with an actual vehicle. There are three main types of simulation that can be run ad hoc or via a scenario runner.
"},{"location":"tutorials/#simulation-methods","title":"Simulation methods","text":""},{"location":"tutorials/#ad-hoc-simulation","title":"Ad hoc simulation","text":"Ad hoc simulation is a flexible method for running basic simulations on your local machine, and is the recommended method for anyone new to Autoware.
"},{"location":"tutorials/#scenario-simulation","title":"Scenario simulation","text":"Scenario simulation uses a scenario runner to run more complex simulations based on predefined scenarios. It is often run automatically for continuous integration purposes, but can also be run on a local machine.
"},{"location":"tutorials/#simulation-types","title":"Simulation types","text":""},{"location":"tutorials/#planning-simulation","title":"Planning simulation","text":"Planning simulation uses simple dummy data to test the Planning and Control components - specifically path generation, path following and obstacle avoidance. It verifies that a vehicle can reach a goal destination while avoiding pedestrians and surrounding cars, and is another method for verifying the validity of Lanelet2 maps. It also allows for testing of traffic light handling.
"},{"location":"tutorials/#how-does-planning-simulation-work","title":"How does planning simulation work?","text":"Rosbag replay simulation uses prerecorded rosbag data to test the following aspects of the Localization and Perception components:
By repeatedly playing back the data, this simulation type can also be used for endurance testing.
"},{"location":"tutorials/#digital-twin-simulation","title":"Digital twin simulation","text":"Digital twin simulation is a simulation type that is able to produce realistic data and simulate almost the entire system. It is also commonly referred to as end-to-end simulation.
"},{"location":"tutorials/ad-hoc-simulation/","title":"Ad hoc simulation","text":""},{"location":"tutorials/ad-hoc-simulation/#ad-hoc-simulation","title":"Ad hoc simulation","text":"Warning
Under Construction
"},{"location":"tutorials/ad-hoc-simulation/planning-simulation/","title":"Planning simulation","text":""},{"location":"tutorials/ad-hoc-simulation/planning-simulation/#planning-simulation","title":"Planning simulation","text":""},{"location":"tutorials/ad-hoc-simulation/planning-simulation/#preparation","title":"Preparation","text":"Download and unpack a sample map.
gdown -O ~/autoware_map/ 'https://docs.google.com/uc?export=download&id=1499_nsbUbIeturZaDj7jhUownh5fvXHd'\nunzip -d ~/autoware_map ~/autoware_map/sample-map-planning.zip\nNote
Sample map: Copyright 2020 TIER IV, Inc.
"},{"location":"tutorials/ad-hoc-simulation/planning-simulation/#basic-simulations","title":"Basic simulations","text":""},{"location":"tutorials/ad-hoc-simulation/planning-simulation/#lane-driving-scenario","title":"Lane driving scenario","text":""},{"location":"tutorials/ad-hoc-simulation/planning-simulation/#1-launch-autoware","title":"1. Launch Autoware","text":"source ~/autoware/install/setup.bash\nros2 launch autoware_launch planning_simulator.launch.xml map_path:=$HOME/autoware_map/sample-map-planning vehicle_model:=sample_vehicle sensor_model:=sample_sensor_kit\nWarning
Note that you cannot use ~ instead of $HOME here.
If ~ is used, the map will fail to load.
a) Click the 2D Pose estimate button in the toolbar, or hit the P key.
b) In the 3D View pane, click and hold the left-mouse button, and then drag to set the direction for the initial pose. An image representing the vehicle should now be displayed.
Warning
Remember to set the initial pose of the car in the same direction as the lane.
To confirm the direction of the lane, check the arrowheads displayed on the map.
"},{"location":"tutorials/ad-hoc-simulation/planning-simulation/#3-set-a-goal-pose-for-the-ego-vehicle","title":"3. Set a goal pose for the ego vehicle","text":"a) Click the 2D Goal Pose button in the toolbar, or hit the G key.
b) In the 3D View pane, click and hold the left-mouse button, and then drag to set the direction for the goal pose. If done correctly, you will see a planned path from initial pose to goal pose.
"},{"location":"tutorials/ad-hoc-simulation/planning-simulation/#4-start-the-ego-vehicle","title":"4. Start the ego vehicle","text":"Now you can start the ego vehicle driving by clicking the AUTO button on OperationMode in AutowareStatePanel. Alteratively, you can manually start the vehicle by running the following command:
source ~/autoware/install/setup.bash\nros2 service call /api/operation_mode/change_to_autonomous autoware_adapi_v1_msgs/srv/ChangeOperationMode {}\nAfter that, you can see AUTONOMOUS sign on OperationMode and AUTO button is grayed out.
Set an initial pose and a goal pose, and engage the ego vehicle.
When the vehicle approaches the goal, it will switch from lane driving mode to parking mode.
After that, the vehicle will reverse into the destination parking spot.
2D Dummy Car or 2D Dummy Pedestrian button in the toolbar.Set the velocity of the object in Tool Properties -> 2D Dummy Car/Pedestrian panel.
!!! note
Changes to the velocity parameter will only affect objects placed after the parameter is changed.
Delete any dummy objects placed in the view by clicking the Delete All Objects button in the toolbar.
Click the Interactive button in the toolbar to make the dummy object interactive.
For adding an interactive dummy object, press SHIFT and click the right click.
ALT and click the right click.For moving an interactive dummy object, hold the right click drag and drop the object.
By default, traffic lights on the map are all treated as if they are set to green. As a result, when a path is created that passed through an intersection with a traffic light, the ego vehicle will drive through the intersection without stopping.
The following steps explain how to set and reset traffic lights in order to test how the Planning component will respond.
"},{"location":"tutorials/ad-hoc-simulation/planning-simulation/#set-traffic-light","title":"Set traffic light","text":"Go to Panels -> Add new panel, select TrafficLightPublishPanel, and then press OK.
In TrafficLightPublishPanel, set the ID and color of the traffic light.
Click the SET button.
Finally, click the PUBLISH button to send the traffic light status to the simulator. Any planned path that goes past the selected traffic light will then change accordingly.
By default, Rviz should display the ID of each traffic light on the map. You can have a closer look at the IDs by zooming in the region or by changing the View type.
In case the IDs are not displayed, try the following troubleshooting steps:
a) In the Displays panel, find the traffic_light_id topic by toggling the triangle icons next to Map > Lanelet2VectorMap > Namespaces.
b) Check the traffic_light_id checkbox.
c) Reload the topic by clicking the Map checkbox twice.
You can update the color of the traffic light by selecting the next color (in the image it is GREEN) and clicking SET button. In the image the traffic light in front of the ego vehicle changed from RED to GREEN and the vehicle restarted.
To remove a traffic light from TrafficLightPublishPanel, click the RESET button.
Reference video tutorials
"},{"location":"tutorials/ad-hoc-simulation/rosbag-replay-simulation/","title":"Rosbag replay simulation","text":""},{"location":"tutorials/ad-hoc-simulation/rosbag-replay-simulation/#rosbag-replay-simulation","title":"Rosbag replay simulation","text":""},{"location":"tutorials/ad-hoc-simulation/rosbag-replay-simulation/#steps","title":"Steps","text":"Download and unpack a sample map.
gdown -O ~/autoware_map/ 'https://docs.google.com/uc?export=download&id=1A-8BvYRX3DhSzkAnOcGWFw5T30xTlwZI'\nunzip -d ~/autoware_map/ ~/autoware_map/sample-map-rosbag.zip\nDownload the sample rosbag files.
gdown -O ~/autoware_map/ 'https://docs.google.com/uc?export=download&id=1VnwJx9tI3kI_cTLzP61ktuAJ1ChgygpG'\nunzip -d ~/autoware_map/ ~/autoware_map/sample-rosbag.zip\nLaunch Autoware.
source ~/autoware/install/setup.bash\nros2 launch autoware_launch logging_simulator.launch.xml map_path:=$HOME/autoware_map/sample-map-rosbag vehicle_model:=sample_vehicle sensor_model:=sample_sensor_kit\nNote that you cannot use ~ instead of $HOME here.
Play the sample rosbag file.
source ~/autoware/install/setup.bash\nros2 bag play ~/autoware_map/sample-rosbag/sample.db3 -r 0.2 -s sqlite3\nTo focus the view on the ego vehicle, change the Target Frame in the RViz Views panel from viewer to base_link.
To switch the view to Third Person Follower etc, change the Type in the RViz Views panel.
Reference video tutorials
"},{"location":"tutorials/ad-hoc-simulation/digital-twin-simulation/MORAI_Sim-tutorial/","title":"MORAI Sim: Drive","text":""},{"location":"tutorials/ad-hoc-simulation/digital-twin-simulation/MORAI_Sim-tutorial/#morai-sim-drive","title":"MORAI Sim: Drive","text":"Note
Any kind of for-profit activity with the trial version of the MORAI SIM:Drive is strictly prohibited.
"},{"location":"tutorials/ad-hoc-simulation/digital-twin-simulation/MORAI_Sim-tutorial/#hardware-requirements","title":"Hardware requirements","text":"Minimum PC Specs OS Windows 10, Ubuntu 20.04, Ubuntu 18.04, Ubuntu 16.04 CPU Intel i5-9600KF or AMD Ryzen 5 3500X RAM DDR4 16GB GPU RTX2060 Super Required PC Specs OS Windows 10, Ubuntu 20.04, Ubuntu 18.04, Ubuntu 16.04 CPU Intel i9-9900K or AMD Ryzen 7 3700X (or higher) RAM DDR4 64GB (or higher) GPU RTX2080Ti or higher"},{"location":"tutorials/ad-hoc-simulation/digital-twin-simulation/MORAI_Sim-tutorial/#application-and-download","title":"Application and Download","text":"Only for AWF developers, trial license for 3 months can be issued. Download the application form and send to Hyeongseok Jeon
After the trial license is issued, you can login to MORAI Sim:Drive via Launchers (Windows/Ubuntu)
CAUTION: Do not use the Launchers in the following manual
"},{"location":"tutorials/ad-hoc-simulation/digital-twin-simulation/MORAI_Sim-tutorial/#technical-documents","title":"Technical Documents","text":"as Oct. 2022, our simulation version is ver.22.R3 but the english manual is under construction.
Be aware that the following manuals are for ver.22.R2
Hyeongseok Jeon will give full technical support
AWSIM is a simulator for Autoware development and testing. To get started, please follow the official instruction provided by TIER IV.
"},{"location":"tutorials/scenario-simulation/","title":"Scenario simulation","text":""},{"location":"tutorials/scenario-simulation/#scenario-simulation","title":"Scenario simulation","text":"Warning
Under Construction
"},{"location":"tutorials/scenario-simulation/planning-simulation/installation/","title":"Installation","text":""},{"location":"tutorials/scenario-simulation/planning-simulation/installation/#installation","title":"Installation","text":"This document contains step-by-step instruction on how to build AWF Autoware Core/Universe with scenario_simulator_v2.
Navigate to the Autoware workspace:
cd autoware\nImport Simulator dependencies:
vcs import src < simulator.repos\nInstall dependent ROS packages:
source /opt/ros/humble/setup.bash\nrosdep install -y --from-paths src --ignore-src --rosdistro $ROS_DISTRO\nBuild the workspace:
colcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release\nNote
Running the Scenario Simulator requires some additional steps on top of building and installing Autoware, so make sure that Scenario Simulator installation has been completed first before proceeding.
"},{"location":"tutorials/scenario-simulation/planning-simulation/random-test-simulation/#running-steps","title":"Running steps","text":"Move to the workspace directory where Autoware and the Scenario Simulator have been built.
Source the workspace setup script:
source install/setup.bash\nRun the simulation:
ros2 launch random_test_runner random_test.launch.py \\\narchitecture_type:=awf/universe \\\nsensor_model:=sample_sensor_kit \\\nvehicle_model:=sample_vehicle\nFor more information about supported parameters, refer to the random_test_runner documentation.
"},{"location":"tutorials/scenario-simulation/planning-simulation/scenario-test-simulation/","title":"Scenario test simulation","text":""},{"location":"tutorials/scenario-simulation/planning-simulation/scenario-test-simulation/#scenario-test-simulation","title":"Scenario test simulation","text":"Note
Running the Scenario Simulator requires some additional steps on top of building and installing Autoware, so make sure that Scenario Simulator installation has been completed first before proceeding.
"},{"location":"tutorials/scenario-simulation/planning-simulation/scenario-test-simulation/#running-steps","title":"Running steps","text":"Move to the workspace directory where Autoware and the Scenario Simulator have been built.
Source the workspace setup script:
source install/setup.bash\nRun the simulation:
ros2 launch scenario_test_runner scenario_test_runner.launch.py \\\narchitecture_type:=awf/universe \\\nrecord:=false \\\nscenario:='$(find-pkg-share scenario_test_runner)/scenario/sample.yaml' \\\nsensor_model:=sample_sensor_kit \\\nvehicle_model:=sample_vehicle\nReference video tutorials
"},{"location":"tutorials/scenario-simulation/rosbag-replay-simulation/driving-log-replayer/","title":"Driving Log Replayer","text":""},{"location":"tutorials/scenario-simulation/rosbag-replay-simulation/driving-log-replayer/#driving-log-replayer","title":"Driving Log Replayer","text":"Driving Log Replayer is an evaluation tool for Autoware. To get started, follow the official instruction provided by TIER IV.
"}]} \ No newline at end of file +{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"Introduction","text":""},{"location":"#autoware-documentation","title":"Autoware Documentation","text":""},{"location":"#about-autoware","title":"About Autoware","text":"Autoware is the world\u2019s leading open-source software project for autonomous driving. Autoware is built on Robot Operating System (ROS) and enables commercial deployment of autonomous driving in a broad range of vehicles and applications.
Please see here for more details.
"},{"location":"#related-documentations","title":"Related Documentations","text":"This Autoware Documentation is for Autoware's general information.
For detailed documents of Autoware Universe components, see Autoware Universe Documentation.
"},{"location":"#getting-started","title":"Getting started","text":"Thank you for your interest in contributing! Autoware is supported by people like you, and all types and sizes of contribution are welcome.
As a contributor, here are the guidelines that we would like you to follow for Autoware and its associated repositories.
Like Autoware itself, these guidelines are being actively developed and suggestions for improvement are always welcome! Guideline changes can be proposed by creating a discussion in the Ideas category.
"},{"location":"contributing/#code-of-conduct","title":"Code of Conduct","text":"To ensure the Autoware community stays open and inclusive, please follow the Code of Conduct.
If you believe that someone in the community has violated the Code of Conduct, please make a report by emailing conduct@autoware.org.
"},{"location":"contributing/#what-should-i-know-before-i-get-started","title":"What should I know before I get started?","text":""},{"location":"contributing/#autoware-concepts","title":"Autoware concepts","text":"To gain a high-level understanding of Autoware's architecture and design, the following pages provide a brief overview:
For experienced developers, the Autoware interfaces and individual component pages should also be reviewed to understand the inputs and outputs for each component or module at a more detailed level.
"},{"location":"contributing/#contributing-to-open-source-projects","title":"Contributing to open source projects","text":"If you are new to open source projects, we recommend reading GitHub's How to Contribute to Open Source guide for an overview of why people contribute to open source projects, what it means to contribute and much more besides.
"},{"location":"contributing/#how-can-i-get-help","title":"How can I get help?","text":"Do not open issues for general support questions as we want to keep GitHub issues for confirmed bug reports. Instead, open a discussion in the Q&A category. For more details on the support mechanisms for Autoware, refer to the Support guidelines.
Note
Issues created for questions or unconfirmed bugs will be moved to GitHub discussions by the maintainers.
"},{"location":"contributing/#how-can-i-contribute","title":"How can I contribute?","text":""},{"location":"contributing/#discussions","title":"Discussions","text":"You can contribute to Autoware by facilitating and participating in discussions, such as:
The various working groups within the Autoware Foundation are responsible for accomplishing goals set by the Technical Steering Committee. These working groups are open to everyone, and joining a particular working group will allow you to gain an understanding of current projects, see how those projects are managed within each group and to contribute to issues that will help progress a particular project.
To see the schedule for upcoming working group meetings, refer to the Autoware Foundation events calendar.
"},{"location":"contributing/#bug-reports","title":"Bug reports","text":"Before you report a bug, please search the issue tracker for the appropriate repository. It is possible that someone has already reported the same issue and that workarounds exist. If you can't determine the appropriate repository, ask the maintainers for help by creating a new discussion in the Q&A category.
When reporting a bug, you should provide a minimal set of instructions to reproduce the issue. Doing so allows us to quickly confirm and focus on the right problem.
If you want to fix the bug by yourself that will be appreciated, but you should discuss possible approaches with the maintainers in the issue before submitting a pull request.
Creating an issue is straightforward, but if you happen to experience any problems then create a Q&A discussion to ask for help.
"},{"location":"contributing/#pull-requests","title":"Pull requests","text":"You can submit pull requests for small changes such as:
If your pull request is a large change, the following process should be followed:
Create a GitHub Discussion to propose the change. Doing so allows you to get feedback from other members and the Autoware maintainers and to ensure that the proposed change is in line with Autoware's design philosophy and current development plans. If you're not sure where to have that conversation, then create a new Q&A discussion.
Create an issue following consensus in the discussions
Create a pull request to implement the changes that references the Issue created in step 2
Create documentation for the new addition (if relevant)
Examples of large changes include:
For more information on how to submit a good pull request, have a read of the pull request guidelines and don't forget to review the required license notations!
"},{"location":"contributing/license/","title":"License","text":""},{"location":"contributing/license/#license","title":"License","text":"Autoware is licensed under Apache License 2.0. Thus all contributions will be licensed as such as per clause 5 of the Apache License 2.0:
5. Submission of Contributions. Unless You explicitly state otherwise,\n any Contribution intentionally submitted for inclusion in the Work\n by You to the Licensor shall be under the terms and conditions of\n this License, without any additional terms or conditions.\n Notwithstanding the above, nothing herein shall supersede or modify\n the terms of any separate license agreement you may have executed\n with Licensor regarding such Contributions.\nHere is an example copyright header to add to the top of a new file:
Copyright [first year of contribution] The Autoware Contributors\nSPDX-License-Identifier: Apache-2.0\nWe don't write copyright notations of each contributor here. Instead, we place them in the NOTICE file like the following.
This product includes code developed by [company name].\nCopyright [first year of contribution] [company name]\nLet us know if your legal department has a special request for the copyright notation.
Currently, the old formats explained here are also acceptable. Those old formats can be replaced by this new format if the original authors agree. Note that we won't write their copyrights to the NOTICE file unless they agree with the new format.
References:
Warning
Under Construction
"},{"location":"contributing/coding-guidelines/#common-guidelines","title":"Common guidelines","text":"Refer to the following links for now:
Also, keep in mind the following concepts.
Warning
Under Construction
Refer to the following links for now:
To reduce duplications in CMakeLists.txt, there is the autoware_package() macro. See the README and use it in your package.
Warning
Under Construction
"},{"location":"contributing/coding-guidelines/languages/cpp/#references","title":"References","text":"Follow the guidelines below if a rule is not defined on this page.
Also, it is encouraged to apply Clang-Tidy to each file. For the usage, see Applying Clang-Tidy to ROS packages.
Note that not all rules are covered by Clang-Tidy.
"},{"location":"contributing/coding-guidelines/languages/cpp/#style-rules","title":"Style rules","text":""},{"location":"contributing/coding-guidelines/languages/cpp/#include-header-files-in-the-defined-order-required-partially-automated","title":"Include header files in the defined order (required, partially automated)","text":""},{"location":"contributing/coding-guidelines/languages/cpp/#rationale","title":"Rationale","text":"Include the headers in the following order:
// Compliant\n#include \"my_header.hpp\"\n\n#include \"my_package/foo.hpp\"\n\n#include <package1/foo.hpp>\n#include <package2/bar.hpp>\n\n#include <std_msgs/msg/header.hpp>\n\n#include <iostream>\n#include <vector>\nIf you use \"\" and <> properly, ClangFormat in pre-commit sorts headers automatically.
Do not define macros between #include lines because it prevents automatic sorting.
// Non-compliant\n#include <package1/foo.hpp>\n#include <package2/bar.hpp>\n\n#define EIGEN_MPL2_ONLY\n#include \"my_header.hpp\"\n#include \"my_package/foo.hpp\"\n\n#include <Eigen/Core>\n\n#include <std_msgs/msg/header.hpp>\n\n#include <iostream>\n#include <vector>\nInstead, define macros before #include lines.
// Compliant\n#define EIGEN_MPL2_ONLY\n\n#include \"my_header.hpp\"\n\n#include \"my_package/foo.hpp\"\n\n#include <Eigen/Core>\n#include <package1/foo.hpp>\n#include <package2/bar.hpp>\n\n#include <std_msgs/msg/header.hpp>\n\n#include <iostream>\n#include <vector>\nIf there are any reasons for defining macros at a specific position, write a comment before the macro.
// Compliant\n#include \"my_header.hpp\"\n\n#include \"my_package/foo.hpp\"\n\n#include <package1/foo.hpp>\n#include <package2/bar.hpp>\n\n#include <std_msgs/msg/header.hpp>\n\n#include <iostream>\n#include <vector>\n\n// For the foo bar reason, the FOO_MACRO must be defined here.\n#define FOO_MACRO\n#include <foo/bar.hpp>\nvoid function_name()\n{\n}\nrosidl file can use other naming conventions.enum class Color\n{\nRed, Green, Blue\n}\nstd::numbers.rosidl file can use other naming conventions.constexpr double gravity = 9.80665;\nclass RosApi;\nRosApi ros_api;\nWarning
Under Construction
Refer to the following links for now:
Warning
Under Construction
Refer to the following links for now:
Warning
Under Construction
Refer to the following links for now:
Warning
Under Construction
Refer to the following links for now:
Warning
Under Construction
Refer to the following links for now:
Warning
Under Construction
Refer to the following links for now:
Warning
Under Construction
"},{"location":"contributing/coding-guidelines/ros-nodes/console-logging/","title":"Console logging","text":""},{"location":"contributing/coding-guidelines/ros-nodes/console-logging/#console-logging","title":"Console logging","text":"ROS 2 logging is a powerful tool for understanding and debugging ROS nodes.
This page focuses on how to design console logging in Autoware and shows several practical examples. To comprehensively understand how ROS 2 logging works, refer to the logging documentation.
"},{"location":"contributing/coding-guidelines/ros-nodes/console-logging/#logging-use-cases-in-autoware","title":"Logging use cases in Autoware","text":"To efficiently support these use cases, clean and highly visible logs are required. For that, several rules are defined below.
"},{"location":"contributing/coding-guidelines/ros-nodes/console-logging/#rules","title":"Rules","text":""},{"location":"contributing/coding-guidelines/ros-nodes/console-logging/#choose-appropriate-severity-levels-required-non-automated","title":"Choose appropriate severity levels (required, non-automated)","text":""},{"location":"contributing/coding-guidelines/ros-nodes/console-logging/#rationale","title":"Rationale","text":"It's confusing if severity levels are inappropriate as follows:
FATAL.INFO.Use the following criteria as a reference:
Some third-party nodes such as drivers may not follow the Autoware's guidelines. If the logs are noisy, unnecessary logs should be filtered out.
"},{"location":"contributing/coding-guidelines/ros-nodes/console-logging/#example_1","title":"Example","text":"Use the --log-level {level} option to change the minimum level of logs to be displayed:
<launch>\n<!-- This outputs only FATAL level logs. -->\n<node pkg=\"demo_nodes_cpp\" exec=\"talker\" ros_args=\"--log-level fatal\" />\n</launch>\nIf you want to disable only specific output targets, use the --disable-stdout-logs, --disable-rosout-logs, and/or --disable-external-lib-logs options:
<launch>\n<!-- This outputs to rosout and disk. -->\n<node pkg=\"demo_nodes_cpp\" exec=\"talker\" ros_args=\"--disable-stdout-logs\" />\n</launch>\n<launch>\n<!-- This outputs to stdout. -->\n<node pkg=\"demo_nodes_cpp\" exec=\"talker\" ros_args=\"--disable-rosout-logs --disable-external-lib-logs\" />\n</launch>\nIf tons of logs are shown on the console, people miss important message.
"},{"location":"contributing/coding-guidelines/ros-nodes/console-logging/#example_2","title":"Example","text":"While waiting for some messages, throttled logs are usually enough. In such cases, wait about 5 seconds as a reference value.
// Compliant\nvoid FooNode::on_timer() {\nif (!current_pose_) {\nRCLCPP_ERROR_THROTTLE(get_logger(), *get_clock(), 5000, \"Waiting for current_pose_.\");\nreturn;\n}\n}\n\n// Non-compliant\nvoid FooNode::on_timer() {\nif (!current_pose_) {\nRCLCPP_ERROR(get_logger(), \"Waiting for current_pose_.\");\nreturn;\n}\n}\nThe following cases are acceptable even if it's not throttled.
Core library classes, which contain reusable algorithms, may also be used for non-ROS platforms. When porting libraries to other platforms, fewer dependencies are preferred.
"},{"location":"contributing/coding-guidelines/ros-nodes/console-logging/#example_3","title":"Example","text":"// Compliant\n#include <rclcpp/logging.hpp>\n\nclass FooCore {\npublic:\nexplicit FooCore(const rclcpp::Logger & logger) : logger_(logger) {}\n\nvoid process() {\nRCLCPP_INFO(logger_, \"message\");\n}\n\nprivate:\nrclcpp::Logger logger_;\n};\n\n// Compliant\n// Note that logs aren't published to `/rosout` if the logger name is different from the node name.\n#include <rclcpp/logging.hpp>\n\nclass FooCore {\nvoid process() {\nRCLCPP_INFO(rclcpp::get_logger(\"foo_core_logger\"), \"message\");\n}\n};\n\n\n// Non-compliant\n#include <rclcpp/node.hpp>\n\nclass FooCore {\npublic:\nexplicit FooCore(const rclcpp::NodeOptions & node_options) : node_(\"foo_core_node\", node_options) {}\n\nvoid process() {\nRCLCPP_INFO(node_.get_logger(), \"message\");\n}\n\nprivate:\nrclcpp::Node node_;\n};\nTo filter logs, using rqt_console is useful:
ros2 run rqt_console rqt_console\nFor more details, refer to ROS 2 Documentation.
"},{"location":"contributing/coding-guidelines/ros-nodes/console-logging/#useful-marco-expressions","title":"Useful marco expressions","text":"To debug program, sometimes you need to see which functions and lines of code are executed. In that case, you can use __FILE__, __LINE__ and __FUNCTION__ macro:
void FooNode::on_timer() {\nRCLCPP_DEBUG(get_logger(), \"file: %s, line: %s, function: %s\" __FILE__, __LINE__, __FUNCTION__);\n}\nThe example output is as follows:
[DEBUG] [1671720414.395456931] [foo]: file: /path/to/file.cpp, line: 100, function: on_timer
"},{"location":"contributing/coding-guidelines/ros-nodes/coordinate-system/","title":"Coordinate system","text":""},{"location":"contributing/coding-guidelines/ros-nodes/coordinate-system/#coordinate-system","title":"Coordinate system","text":""},{"location":"contributing/coding-guidelines/ros-nodes/coordinate-system/#overview","title":"Overview","text":"The commonly used coordinate systems include the world coordinate system, the vehicle coordinate system, and the sensor coordinate system.
In Autoware, coordinate systems are typically used to represent the position and movement of vehicles and obstacles in space. Coordinate systems are commonly used for path planning, perception and control, can help the vehicle decide how to avoid obstacles and to plan a safe and efficient path of travel.
Transformation of sensor data
In Autoware, each sensor has a unique coordinate system and their data is expressed in terms of the coordinates. In order to correlate the independent data between different sensors, we need to find the position relationship between each sensor and the vehicle body. Once the installation position of the sensor on the vehicle body is determined, it will remain fixed during running, so the offline calibration method can be used to determine the precise position of each sensor relative to the vehicle body.
ROS TF2
The TF2 system maintains a tree of coordinate transformations to represent the relationships between different coordinate systems. Each coordinate system is given a unique name and they are connected by coordinate transformations. How to use TF2, refer to the TF2 tutorial.
In Autoware, a common coordinate system structure is shown below:
graph TD\n /earth --> /map\n /map --> /base_link\n /base_link --> /imu\n /base_link --> /lidar\n /base_link --> /gnss\n /base_link --> /radar\n /base_link --> /camera_link\n /camera_link --> /camera_optical_linkearth coordinate system describe the position of any point on the earth in terms of geodetic longitude, latitude, and altitude. In Autoware, the earth frame is only used in the GnssInsPositionStamped message.map coordinate system is used to represent the location of points on a local map. Geographical coordinate system are mapped into plane rectangular coordinate system using UTM or MGRS. The map frame`s axes point to the East, North, Up directions as explained in Coordinate Axes Conventions.camera_link is ROS standard camera coordinate system .camera_optical_link is image standard camera coordinate system.base_link frame by using the other sensors","text":"Generally we don't have the localization sensors physically at the base_link frame. So various sensors localize with respect to their own frames, let's call it sensor frame.
We introduce a new frame naming convention: x_by_y:
x: estimated frame name\ny: localization method/source\nWe cannot directly get the sensor frame. Because we would need the EKF module to estimate the base_link frame first.
Without the EKF module the best we can do is to estimate Map[map] --> sensor_by_sensor --> base_link_by_sensor using this sensor.
For the integrated GNSS/INS we use the following frames:
flowchart LR\n earth --> Map[map] --> gnss_ins_by_gnss_ins --> base_link_by_gnss_insThe gnss_ins_by_gnss_ins frame is obtained by the coordinates from GNSS/INS sensor. The coordinates are converted to map frame using the gnss_poser node.
Finally gnss_ins_by_gnss_ins frame represents the position of the gnss_ins estimated by the gnss_ins sensor in the map.
Then by using the static transformation between gnss_ins and the base_link frame, we can obtain the base_link_by_gnss_ins frame. Which represents the base_link estimated by the gnss_ins sensor.
References:
We are using East, North, Up (ENU) coordinate axes convention by default throughout the stack.
X+: East\nY+: North\nZ+: Up\nThe position, orientation, velocity, acceleration are all defined in the same axis convention.
Position by the GNSS/INS sensor is expected to be in earth frame.
Orientation, velocity, acceleration by the GNSS/INS sensor are expected to be in the sensor frame. Axes parallel to the map frame.
If roll, pitch, yaw is provided, they correspond to rotation around X, Y, Z axes respectively.
Rotation around:\nX+: roll\nY+: pitch\nZ+: yaw\nReferences:
Calibration of sensor
The conversion relationship between every sensor coordinate system and base_link can be obtained through sensor calibration technology. How to calibrating your sensors refer to this link calibrating your sensors.
Localization
The relationship between the base_link coordinate system and the map coordinate system is determined by the position and orientation of the vehicle, and can be obtained from the vehicle localization result.
Geo-referencing of map data
The geo-referencing information can get the transformation relationship of earth coordinate system to local map coordinate system.
Warning
Under Construction
"},{"location":"contributing/coding-guidelines/ros-nodes/directory-structure/#c-package","title":"C++ package","text":"<package_name>\n\u251c\u2500 config\n\u2502 \u251c\u2500 foo_ros.param.yaml\n\u2502 \u2514\u2500 foo_non_ros.yaml\n\u251c\u2500 include\n\u2502 \u2514\u2500 <package_name>\n\u2502 \u2514\u2500 foo_public.hpp\n\u251c\u2500 launch\n\u2502 \u251c\u2500 foo.launch.xml\n\u2502 \u2514\u2500 foo.launch.py\n\u251c\u2500 src\n\u2502 \u251c\u2500 foo_node.cpp\n\u2502 \u251c\u2500 foo_node.hpp\n\u2502 \u2514\u2500 foo_private.hpp\n\u251c\u2500 test\n\u2502 \u2514\u2500 test_foo.cpp\n\u251c\u2500 package.xml\n\u2514\u2500 CMakeLists.txt\nPlace configuration files such as node parameters.
For ROS parameters, use the extension .param.yaml. For non-ROS parameters, use the extension .yaml.
Rationale: Since ROS parameters files are type-sensitive, they should not be the target of some code formatters and linters. In order to distinguish the file type, we use different file extensions.
"},{"location":"contributing/coding-guidelines/ros-nodes/directory-structure/#include-directory","title":"include directory","text":"Place header files exposed to other packages. Do not place files directly under the include directory, but place files under the directory with the package name. This directory is used for mostly library headers. Note that many headers do not need to be placed here. It is enough to place the headers under the src directory.
Reference: https://docs.ros.org/en/rolling/How-To-Guides/Ament-CMake-Documentation.html#adding-files-and-headers
"},{"location":"contributing/coding-guidelines/ros-nodes/directory-structure/#launch-directory","title":"launch directory","text":"Place launch files (.launch.xml and .launch.py).
Place source files and private header files.
"},{"location":"contributing/coding-guidelines/ros-nodes/directory-structure/#test-directory","title":"test directory","text":"Place source files for testing.
"},{"location":"contributing/coding-guidelines/ros-nodes/directory-structure/#python-package","title":"Python package","text":"T.B.D.
"},{"location":"contributing/coding-guidelines/ros-nodes/launch-files/","title":"Launch files","text":""},{"location":"contributing/coding-guidelines/ros-nodes/launch-files/#launch-files","title":"Launch files","text":""},{"location":"contributing/coding-guidelines/ros-nodes/launch-files/#overview","title":"Overview","text":"Autoware use ROS2 launch system to startup the software. Please see the official documentation to get a basic understanding about ROS 2 Launch system if you are not familiar with it.
"},{"location":"contributing/coding-guidelines/ros-nodes/launch-files/#guideline","title":"Guideline","text":""},{"location":"contributing/coding-guidelines/ros-nodes/launch-files/#the-organization-of-launch-files-in-autoware","title":"The organization of launch files in Autoware","text":"Autoware mainly has two repositories related to launch file organization: the autoware.universe and the autoware_launch.
"},{"location":"contributing/coding-guidelines/ros-nodes/launch-files/#autowareuniverse","title":"autoware.universe","text":"the autoware.universe contains the code of the main Autoware modules, and its launch directory is responsible for launching the nodes of each module. Autoware software stack is organized based on the architecture, so you may find that we try to match the launch structure similar to the architecture (splitting of files, namespace). For example, the tier4_map_launch subdirectory corresponds to the map module, so do the other tier4_*_launch subdirectories.
The autoware_launch is a repository referring to autoware.universe. The mainly purpose of introducing this repository is to provide the general entrance to start the Autoware software stacks, i.e, calling the launch file of each module.
The autoware.launch.xml is the basic launch file for road driving scenarios.
As can be seen from the content, the entire launch file is divided into several different modules, including Vehicle, System, Map, Sensing, Localization, Perception, Planning, Control, etc. By setting the launch_* argument equals to true or false , we can determine which modules to be loaded.
logging_simulator.launch.xml is often used together with the recorded ROS bag to debug if the target module (e.g, Sensing, Localization or Perception) functions normally.planning_simulator.launch.xml is based on the Planning Simulator tool, mainly used for testing/validation of Planning module by simulating traffic rules, interactions with dynamic objects and control commands to the ego vehicle.e2e_simulator.launch.xml is the launcher for digital twin simulation environment.graph LR\nA11[logging_simulator.launch.xml]-.->A10[autoware.launch.xml]\nA12[planning_simulator.launch.xml]-.->A10[autoware.launch.xml]\nA13[e2e_simulator.launch.xml]-.->A10[autoware.launch.xml]\n\nA10-->A21[tier4_map_component.launch.xml]\nA10-->A22[xxx.launch.py]\nA10-->A23[tier4_localization_component.launch.xml]\nA10-->A24[xxx.launch.xml]\nA10-->A25[tier4_sensing_component.launch.xml]\n\nA23-->A30[localization.launch.xml]\nA30-->A31[pose_estimator.launch.xml]\nA30-->A32[util.launch.xml]\nA30-->A33[pose_twist_fusion_filter.launch.xml]\nA30-->A34[xxx.launch.xml]\nA30-->A35[twist_estimator.launch.xml]\n\nA33-->A41[stop_filter.launch.xml]\nA33-->A42[ekf_localizer.launch.xml]\nA33-->A43[twist2accel.launch.xml]If a newly created package has executable node, we expect sample launch file and configuration within the package, just like the recommended structure shown in previous directory structure page.
In order to automatically load the newly added package when starting Autoware, you need to make some necessary changes to the corresponding launch file. For example, if using ICP instead of NDT as the pointcloud registration algorithm, you can modify the autoware.universe/launch/tier4_localization_launch/launch/pose_estimator/pose_estimator.launch.xml file to load the newly added ICP package.
Another purpose of introducing the autoware_launch repository is to facilitate the parameter management of Autoware. Thinking about this situation: if we want to integrate Autoware to a specific vehicle and modify parameters, we have to fork autoware.universe which also has a lot of code other than parameters and is frequently updated by developers. By intergrating these parameters in autoware_launch, we can customize the Autoware parameters just by forking autoware_launch repository. Taking the localization module as an examples:
autoware_launch/autoware_launch/config/localization.autoware_launch/autoware_launch/launch/components/tier4_localization_component.launch.xml file.autoware.universe/launch/tier4_localization_launch/launch, the launch files loads the \u201claunch parameters\u201d if the argument is given in the parameter configuration file. You can still use the default parameters in each packages to launch tier4_localization_launch within autoware.universe.All messages should follow ROS message description specification.
The accepted formats are:
.msg.srv.actionUnder Construction
Use Array as a suffix when creating a plural type of a message. This suffix is commonly used in common_interfaces.
All the fields by default have the following units depending on their types:
type default unit distance meter (m) angle radians (rad) time second (s) speed m/s velocity m/s acceleration m/s\u00b2 angular vel. rad/s angular accel. rad/s\u00b2If a field in a message has any of these default units, don't add any suffix or prefix denoting the type.
"},{"location":"contributing/coding-guidelines/ros-nodes/message-guidelines/#non-default-units","title":"Non-default units","text":"For non-default units, use following suffixes:
type non-default unit suffix distance nanometer_nm distance micrometer _um distance millimeter _mm distance kilometer _km angle degree (deg) _deg time nanosecond _ns time microsecond _us time millisecond _ms time minute _min time hour (h) _hour velocity km/h _kmph If a unit that you'd like to use doesn't exist here, create an issue/PR to add it to this list.
"},{"location":"contributing/coding-guidelines/ros-nodes/message-guidelines/#message-field-types","title":"Message field types","text":"For list of types supported by the ROS interfaces see here.
Also copied here for convenience:
Message Field Type C++ equivalentbool bool byte uint8_t char char float32 float float64 double int8 int8_t uint8 uint8_t int16 int16_t uint16 uint16_t int32 int32_t uint32 uint32_t int64 int64_t uint64 uint64_t string std::string wstring std::u16string"},{"location":"contributing/coding-guidelines/ros-nodes/message-guidelines/#arrays","title":"Arrays","text":"For arrays, use unbounded dynamic array type.
Example:
int32[] unbounded_integer_array\nROS 2 interfaces don't support enumerations directly.
It is possible to define integers constants and assign them to a non-constant integer parameter.
Constants are written in CONSTANT_CASE.
Assign a different value to each element of a constant.
Example from shape_msgs/msg/SolidPrimitive.msg
uint8 BOX=1\nuint8 SPHERE=2\nuint8 CYLINDER=3\nuint8 CONE=4\nuint8 PRISM=5\n\n# The type of the shape\nuint8 type\nOn top of the message, briefly explain what the message contains and/or what it is used for. For an example, see sensor_msgs/msg/Imu.msg.
If necessary, add line comments before the fields that explain the context and/or meaning.
For simple fields like x, y, z, w you might not need to add comments.
Even though it is not strictly checked, try not to pass 100 characters in a line.
Example:
# Number of times the vehicle performed an emergency brake\nuint32 count_emergency_brake\n\n# Seconds passed since the last emergency brake\nuint64 duration\nfloat32 path_length_mfloat32 path_lengthfloat32 kmph_velocity_vehiclefloat32 velocity_vehicle_kmphfloat32 velocity_vehicle_km_hfloat32 velocity_vehicle_kmphThe ROS packages in Autoware have ROS parameters. You need to customize the parameters depending on your applications. It is recommended not to set default values when declaring ROS parameters to avoid unintended behaviors due to accidental use of default values. Instead, set parameters from configuration files named *.param.yaml.
For understanding ROS 2 parameters, also check out the official documentation Understanding parameters.
"},{"location":"contributing/coding-guidelines/ros-nodes/parameters/#parameter-files","title":"Parameter files","text":"Autoware has the following two types of parameter files for ROS packages:
behavior_path_plannerFOO_package, the parameter is expected to be stored in FOO_package/config.<launch>\n<arg name=\"foo_node_param_path\" default=\"$(find-pkg-share FOO_package)/config/foo_node.param.yaml\" />\n\n<node pkg=\"FOO_package\" exec=\"foo_node\">\n...\n <param from=\"$(var foo_node_param_path)\" />\n</node>\n</launch>\nbehavior_path_planner stored under autoware_launchautoware_launch.All the parameter files should have the .param.yaml suffix so that the auto-format can be applied properly.
Warning
Under Construction
"},{"location":"contributing/coding-guidelines/ros-nodes/topic-namespaces/","title":"Topic namespaces","text":""},{"location":"contributing/coding-guidelines/ros-nodes/topic-namespaces/#topic-namespaces","title":"Topic namespaces","text":""},{"location":"contributing/coding-guidelines/ros-nodes/topic-namespaces/#overview","title":"Overview","text":"ROS allows topics, parameters and nodes to be namespaced which provides the following benefits:
This page focuses on how to use namespaces in Autoware and shows some useful examples. For basic information on topic namespaces, refer to this tutorial.
"},{"location":"contributing/coding-guidelines/ros-nodes/topic-namespaces/#how-topics-should-be-named-in-node","title":"How topics should be named in node","text":"Autoware divides the node into the following functional categories, and adds the start namespace for the nodes according to the categories.
When a node is run in a namespace, all topics which that node publishes are given that same namespace. All nodes in the Autoware stack must support namespaces by avoiding practices such as publishing topics in the global namespace.
In general, topics should be namespaced based on the function of the node which produces them and not the node (or nodes) which consume them.
Classify topics as input or output topics based on they are subscribed or published by the node. In the node, input topic is named input/topic_name and output topic is named output/topic_name.
Configure the topic in the node's launch file. Take the joy_controller node as an example, in the following example, set the input and output topics and remap topics in the joy_controller.launch.xml file.
<launch>\n<arg name=\"input_joy\" default=\"/joy\"/>\n<arg name=\"input_odometry\" default=\"/localization/kinematic_state\"/>\n\n<arg name=\"output_control_command\" default=\"/external/$(var external_cmd_source)/joy/control_cmd\"/>\n<arg name=\"output_external_control_command\" default=\"/api/external/set/command/$(var external_cmd_source)/control\"/>\n<arg name=\"output_shift\" default=\"/api/external/set/command/$(var external_cmd_source)/shift\"/>\n<arg name=\"output_turn_signal\" default=\"/api/external/set/command/$(var external_cmd_source)/turn_signal\"/>\n<arg name=\"output_heartbeat\" default=\"/api/external/set/command/$(var external_cmd_source)/heartbeat\"/>\n<arg name=\"output_gate_mode\" default=\"/control/gate_mode_cmd\"/>\n<arg name=\"output_vehicle_engage\" default=\"/vehicle/engage\"/>\n\n<node pkg=\"joy_controller\" exec=\"joy_controller\" name=\"joy_controller\" output=\"screen\">\n<remap from=\"input/joy\" to=\"$(var input_joy)\"/>\n<remap from=\"input/odometry\" to=\"$(var input_odometry)\"/>\n\n<remap from=\"output/control_command\" to=\"$(var output_control_command)\"/>\n<remap from=\"output/external_control_command\" to=\"$(var output_external_control_command)\"/>\n<remap from=\"output/shift\" to=\"$(var output_shift)\"/>\n<remap from=\"output/turn_signal\" to=\"$(var output_turn_signal)\"/>\n<remap from=\"output/gate_mode\" to=\"$(var output_gate_mode)\"/>\n<remap from=\"output/heartbeat\" to=\"$(var output_heartbeat)\"/>\n<remap from=\"output/vehicle_engage\" to=\"$(var output_vehicle_engage)\"/>\n</node>\n</launch>\nHave ~ so that namespace in launch configuration is applied(should not start from root /).
Have ~/input ~/output namespace before topic name used to communicate with other nodes.
e.g., In node obstacle_avoidance_planner, using topic names of type ~/input/topic_name to subscribe to topics.
objects_sub_ = create_subscription<PredictedObjects>(\n\"~/input/objects\", rclcpp::QoS{10},\nstd::bind(&ObstacleAvoidancePlanner::onObjects, this, std::placeholders::_1));\ne.g., In node obstacle_avoidance_planner, using topic names of type ~/output/topic_name to publish topic.
traj_pub_ = create_publisher<Trajectory>(\"~/output/path\", 1);\nVisualization or debug purpose topics should have ~/debug/ namespace.
e.g., In node obstacle_avoidance_planner, in order to debug or visualizing topics, using topic names of type ~/debug/topic_name to publish information.
debug_markers_pub_ =\ncreate_publisher<visualization_msgs::msg::MarkerArray>(\"~/debug/marker\", durable_qos);\n\ndebug_msg_pub_ =\ncreate_publisher<tier4_debug_msgs::msg::StringStamped>(\"~/debug/calculation_time\", 1);\nThe launch configured namespace will be add the topics before, so the topic names will be as following:
/planning/scenario_planning/lane_driving/motion_planning/obstacle_avoidance_planner/debug/marker /planning/scenario_planning/lane_driving/motion_planning/obstacle_avoidance_planner/debug/calculation_time
Rationale: we want to make topic names remapped and configurable from launch files.
Warning
Under Construction
Refer to the following links for now:
Contributions to Autoware's documentation are welcome, and the same principles described in the contribution guidelines should be followed. Small, limited changes can be made by forking this repository and submitting a pull request, but larger changes should be discussed with the community and Autoware maintainers via GitHub Discussion first.
Examples of small changes include:
Examples of larger changes include:
You should refer to the Google developer documentation style guide as much as possible. Reading the Highlights page of that guide is recommended, but if not then the key points below should be noted.
There are two ways to preview your modification on a documentation website.
"},{"location":"contributing/documentation-guidelines/#1-using-github-actions-workflow","title":"1. Using GitHub Actions workflow","text":"Follow the steps below.
deploy-docs label from the sidebar (See below figure).github-actions bot will notify the URL for the pull request's preview.Instead of creating a PR, you can use the mkdocs command to build Autoware's documentation websites on your local computer. Assuming that you are using Ubuntu OS, run the following to install the required libraries.
python3 -m pip install -U $(curl -fsSL https://raw.githubusercontent.com/autowarefoundation/autoware-github-actions/main/deploy-docs/mkdocs-requirements.txt)\nThen, run mkdocs serve on your documentation directory.
cd /PATH/TO/YOUR-autoware-documentation\nmkdocs serve\nIt will launch the MkDocs server. Access http://127.0.0.1:8000/ to see the preview of the website.
"},{"location":"contributing/pull-request-guidelines/","title":"Pull request guidelines","text":""},{"location":"contributing/pull-request-guidelines/#pull-request-guidelines","title":"Pull request guidelines","text":""},{"location":"contributing/pull-request-guidelines/#general-pull-request-workflow","title":"General pull request workflow","text":"Autoware uses the fork-and-pull model. For more details about the model, refer to GitHub Docs.
The following is a general example of the pull request workflow based on the fork-and-pull model. Use this workflow as a reference when you contribute to Autoware.
There are two types of templates. Select one based on the following condition.
maintainer information in package.xml..github/CODEOWNERS file of the repository.@autoware-maintainers.feat(trajectory_follower): add an awesome feature\nNote
You have to start the description part (here add an awesome feature) with a lowercase.
If your change breaks some interfaces, use the ! (breaking changes) mark as follows:
feat(trajectory_follower)!: remove package\nfeat(trajectory_follower)!: change parameter names\nfeat(planning)!: change topic names\nfeat(autoware_utils)!: change function names\nFor the repositories that contain code (most repositories), use the definition of conventional-commit-types for the type.
For documentation repositories such as autoware-documentation, use the following definition:
featfixrefactordocsbuild! (breaking changes)perf and test are generally unused. Other types have the same meaning as the code repositories.
For ROS packages, adding the package name or component name is good.
feat(trajectory_follower): add an awesome feature\nrefactor(planning, control): use common utils\nIt is acceptable if it is agreed with maintainers that there is no other way but to submit a big pull request.
"},{"location":"contributing/pull-request-guidelines/#example_4","title":"Example","text":"feat, fix, refactor, etc.) of changes in the same commit.@{some-of-developers} Would it be possible for you to review this PR?\n@autoware-maintainers friendly ping.\nAutoware has several checks for a pull request. The results are shown at the bottom of the pull request page as below.
If the \u274c mark is shown, click the Details button and investigate the failure reason.
If the Required mark is shown, you cannot merge the pull request unless you resolve the error. If not, it is optional, but preferably it should be fixed.
The following sections explain about common CI checks in Autoware. Note that some repositories may have different settings.
"},{"location":"contributing/pull-request-guidelines/ci-checks/#dco","title":"DCO","text":"The Developer Certificate of Origin (DCO) is a lightweight way for contributors to certify that they wrote or otherwise have the right to submit the code they are contributing to the project.
This workflow checks whether the pull request fulfills DCO. You need to confirm the required items and commit with git commit -s.
For more information, refer to the GitHub App page.
"},{"location":"contributing/pull-request-guidelines/ci-checks/#semantic-pull-request","title":"semantic-pull-request","text":"This workflow checks whether the pull request follows Conventional Commits.
For the detailed rules, see the pull request rules.
"},{"location":"contributing/pull-request-guidelines/ci-checks/#pre-commit","title":"pre-commit","text":"pre-commit is a tool to run formatters or linters when you commit.
This workflow checks whether the pull request has no error with pre-commit.
In the workflow pre-commit.ci - pr is enabled in the repository, it will automatically fix errors by pre-commit.ci as many as possible. If there are some errors remain, fix them manually.
You can run pre-commit in your local environment by the following command:
pre-commit run -a\nOr you can install pre-commit to the repository and automatically run it before committing:
pre-commit install\nSince it is difficult to detect errors with no false positives, some jobs are split into another config file and marked as optional. To check them, use the --config option:
pre-commit run -a --config .pre-commit-config-optional.yaml\nThis workflow detects spelling mistakes using CSpell with our dictionary file. You can submit pull requests to tier4/autoware-spell-check-dict to update the dictionary.
Since it is difficult to detect errors with no false positives, it is an optional workflow, but it is preferable to remove spelling mistakes as many as possible.
"},{"location":"contributing/pull-request-guidelines/ci-checks/#build-and-test-differential","title":"build-and-test-differential","text":"This workflow checks colcon build and colcon test for the pull request. To make the CI faster, it doesn't check all packages but only modified packages and the dependencies.
This workflow is the ARM64 version of build-and-test-differential. You need to add the ARM64 label to run this workflow.
For reference information, since ARM machines are not supported by GitHub-hosted runners, we use self-hosted runners prepared by the AWF. For the details about self-hosted runners, refer to GitHub Docs.
"},{"location":"contributing/pull-request-guidelines/ci-checks/#deploy-docs","title":"deploy-docs","text":"This workflow deploys the preview documentation site for the pull request. You need to add the deploy-docs label to run this workflow.
If there are no corresponding issues, you can ignore this rule.
"},{"location":"contributing/pull-request-guidelines/commit-guidelines/#example","title":"Example","text":"123-add-feature\ndash-case for the separator of branch names (advisory, non-automated)","text":""},{"location":"contributing/pull-request-guidelines/commit-guidelines/#rationale_1","title":"Rationale","text":"123-add-feature\nIf you have already submitted a pull request, you do not have to change the branch name because you need to re-create a pull request, which is noisy and a waste of time. Be careful from the next time.
"},{"location":"contributing/pull-request-guidelines/commit-guidelines/#example_2","title":"Example","text":"Usually it is good to start with a verb.
123-fix-memory-leak-of-trajectory-follower\nDevelopers must certify that they wrote or otherwise have the right to submit the code they are contributing to the project.
"},{"location":"contributing/pull-request-guidelines/commit-guidelines/#rationale_3","title":"Rationale","text":"If not, it will lead to complex license problems.
"},{"location":"contributing/pull-request-guidelines/commit-guidelines/#example_3","title":"Example","text":"git commit -s\nfeat: add a feature\n\nSigned-off-by: Autoware <autoware@example.com>\nWarning
Under Construction
Refer to the following links for now:
There might be some annotations or review comments in the diff view during your review.
To toggle annotations, press the A key.
Before:
After:
To toggle review comments, press the I key.
For other keyboard shortcuts, refer to GitHub Docs.
"},{"location":"contributing/pull-request-guidelines/review-tips/#view-code-in-the-web-based-visual-studio-code","title":"View code in the web-based Visual Studio Code","text":"You can open Visual Studio Code from your browser to view code in a rich UI. To use it, press the . key on any repository or pull request.
For more detailed usage, refer to github/dev.
"},{"location":"contributing/pull-request-guidelines/review-tips/#check-out-the-branch-of-a-pull-request-quickly","title":"Check out the branch of a pull request quickly","text":"If you want to check out the branch of a pull request, it's generally troublesome with the fork-and-pull model.
# Copy the user name and the fork URL.\ngit remote add {user-name} {fork-url}\ngit checkout {user-name}/{branch-name}\ngit remote rm {user-name} # To clean up\nInstead, you can use GitHub CLI to simplify the steps, just run gh pr checkout {pr-number}.
You can copy the command from the top right of the pull request page.
"},{"location":"contributing/testing-guidelines/","title":"Testing guidelines","text":""},{"location":"contributing/testing-guidelines/#testing-guidelines","title":"Testing guidelines","text":""},{"location":"contributing/testing-guidelines/#unit-testing","title":"Unit testing","text":"Unit testing is a software testing method that tests individual units of source code to determine whether they satisfy the specification.
For details, see the Unit testing guidelines.
"},{"location":"contributing/testing-guidelines/#integration-testing","title":"Integration testing","text":"Integration testing combines and tests the individual software modules as a group, and is done after unit testing.
While performing integration testing, the following subtypes of tests are written:
For details, see the Integration testing guidelines.
"},{"location":"contributing/testing-guidelines/integration-testing/","title":"Integration testing","text":""},{"location":"contributing/testing-guidelines/integration-testing/#integration-testing","title":"Integration testing","text":"An integration test is defined as the phase in software testing where individual software modules are combined and tested as a group. Integration tests occur after unit tests, and before validation tests.
The input to an integration test is a set of independent modules that have been unit tested. The set of modules is tested against the defined integration test plan, and the output is a set of properly integrated software modules that is ready for system testing.
"},{"location":"contributing/testing-guidelines/integration-testing/#value-of-integration-testing","title":"Value of integration testing","text":"Integration tests determine if independently developed software modules work correctly when the modules are connected to each other. In ROS 2, the software modules are called nodes. Testing a single node is a special type of integration test that is commonly referred to as component testing.
Integration tests help to find the following types of errors:
malloc failures. This can be tested using tools like stress and udpreplay to test the performance of nodes with real data.With ROS 2, it is possible to program complex autonomous-driving applications with a large number of nodes. Therefore, a lot of effort has been made to provide an integration-test framework that helps developers test the interaction of ROS 2 nodes.
"},{"location":"contributing/testing-guidelines/integration-testing/#integration-test-framework","title":"Integration-test framework","text":"A typical integration-test framework has three parts:
In Autoware, we use the launch_testing framework.
"},{"location":"contributing/testing-guidelines/integration-testing/#smoke-tests","title":"Smoke tests","text":"Autoware has a dedicated API for smoke testing. To use this framework, in package.xml add:
<test_depend>autoware_testing</test_depend>\nAnd in CMakeLists.txt add:
if(BUILD_TESTING)\nfind_package(autoware_testing REQUIRED)\nadd_smoke_test(${PROJECT_NAME} ${NODE_NAME})\nendif()\nDoing so adds smoke tests that ensure that a node can be:
SIGTERM signal.For the full API documentation, refer to the package design page.
Note
This API is not suitable for all smoke test cases. It cannot be used when a specific file location (eg: for a map) is required to be passed to the node, or if some preparation needs to be conducted before node launch. In such cases use the manual solution from the component test section below.
"},{"location":"contributing/testing-guidelines/integration-testing/#integration-test-with-a-single-node-component-test","title":"Integration test with a single node: component test","text":"The simplest scenario is a single node. In this case, the integration test is commonly referred to as a component test.
To add a component test to an existing node, you can follow the example of the lanelet2_map_loader in the map_loader package (added in this PR).
In package.xml, add:
<test_depend>ros_testing</test_depend>\nIn CMakeLists.txt, add or modify the BUILD_TESTING section:
if(BUILD_TESTING)\nadd_ros_test(\ntest/lanelet2_map_loader_launch.test.py\nTIMEOUT \"30\"\n)\ninstall(DIRECTORY\ntest/data/\nDESTINATION share/${PROJECT_NAME}/test/data/\n)\nendif()\nIn addition to the command add_ros_test, we also install any data that is required by the test using the install command.
Note
TIMEOUT argument is given in seconds; see the add_ros_test.cmake file for details.add_ros_test command will run the test in a unique ROS_DOMAIN_ID which avoids interference between tests running in parallel.To create a test, either read the launch_testing quick-start example, or follow the steps below.
Taking test/lanelet2_map_loader_launch.test.py as an example, first dependencies are imported:
import os\nimport unittest\n\nfrom ament_index_python import get_package_share_directory\nimport launch\nfrom launch import LaunchDescription\nfrom launch_ros.actions import Node\nimport launch_testing\nimport pytest\nThen a launch description is created to launch the node under test. Note that the test_map.osm file path is found and passed to the node, something that cannot be done with the smoke testing API:
@pytest.mark.launch_test\ndef generate_test_description():\n\n lanelet2_map_path = os.path.join(\n get_package_share_directory(\"map_loader\"), \"test/data/test_map.osm\"\n )\n\n lanelet2_map_loader = Node(\n package=\"map_loader\",\n executable=\"lanelet2_map_loader\",\n parameters=[{\"lanelet2_map_path\": lanelet2_map_path}],\n )\n\n context = {}\n\n return (\n LaunchDescription(\n [\n lanelet2_map_loader,\n # Start test after 1s - gives time for the map_loader to finish initialization\n launch.actions.TimerAction(\n period=1.0, actions=[launch_testing.actions.ReadyToTest()]\n ),\n ]\n ),\n context,\n )\nNote
TimerAction to delay the start of the test by 1s.context is empty but it can be used to pass objects to the test cases.context in the ROS 2 context_launch_test.py test example.Finally, a test is executed after the node executable has been shut down (post_shutdown_test). Here we ensure that the node was launched without error and exited cleanly.
@launch_testing.post_shutdown_test()\nclass TestProcessOutput(unittest.TestCase):\n def test_exit_code(self, proc_info):\n # Check that process exits with code 0: no error\n launch_testing.asserts.assertExitCodes(proc_info)\nContinuing the example from above, first build your package:
colcon build --packages-up-to map_loader\nsource install/setup.bash\nThen either execute the component test manually:
ros2 test src/universe/autoware.universe/map/map_loader/test/lanelet2_map_loader_launch.test.py\nOr as part of testing the entire package:
colcon test --packages-select map_loader\nVerify that the test is executed; e.g.
$ colcon test-result --all --verbose\n...\nbuild/map_loader/test_results/map_loader/test_lanelet2_map_loader_launch.test.py.xunit.xml: 1 test, 0 errors, 0 failures, 0 skipped\nThe simple test described in Integration test with a single node: component test can be extended in numerous directions, such as testing a node's output.
"},{"location":"contributing/testing-guidelines/integration-testing/#testing-the-output-of-a-node","title":"Testing the output of a node","text":"To test while the node is running, create an active test by adding a subclass of Python's unittest.TestCase to *launch.test.py. Some boilerplate code is required to access output by creating a node and a subscription to a particular topic, e.g.
import unittest\n\nclass TestRunningDataPublisher(unittest.TestCase):\n\n @classmethod\n def setUpClass(cls):\n cls.context = Context()\n rclpy.init(context=cls.context)\n cls.node = rclpy.create_node(\"test_node\", context=cls.context)\n\n @classmethod\n def tearDownClass(cls):\n rclpy.shutdown(context=cls.context)\n\n def setUp(self):\n self.msgs = []\n sub = self.node.create_subscription(\n msg_type=my_msg_type,\n topic=\"/info_test\",\n callback=self._msg_received\n )\n self.addCleanup(self.node.destroy_subscription, sub)\n\n def _msg_received(self, msg):\n # Callback for ROS 2 subscriber used in the test\n self.msgs.append(msg)\n\n def get_message(self):\n startlen = len(self.msgs)\n\n executor = rclpy.executors.SingleThreadedExecutor(context=self.context)\n executor.add_node(self.node)\n\n try:\n # Try up to 60 s to receive messages\n end_time = time.time() + 60.0\n while time.time() < end_time:\n executor.spin_once(timeout_sec=0.1)\n if startlen != len(self.msgs):\n break\n\n self.assertNotEqual(startlen, len(self.msgs))\n return self.msgs[-1]\n finally:\n executor.remove_node(self.node)\n\n def test_message_content():\n msg = self.get_message()\n self.assertEqual(msg, \"Hello, world\")\nUnit testing is the first phase of testing and is used to validate units of source code such as classes and functions. Typically, a unit of code is tested by validating its output for various inputs. Unit testing helps ensure that the code behaves as intended and prevents accidental changes of behavior.
Autoware uses the ament_cmake framework to build and run tests. The same framework is also used to analyze the test results.
ament_cmake provides several convenience functions to make it easy to register tests in a CMake-based package and to ensure that JUnit-compatible result files are generated. It currently supports a few different testing frameworks like pytest, gtest, and gmock.
In order to prevent tests running in parallel from interfering with each other when publishing and subscribing to ROS topics, it is recommended to use commands from ament_cmake_ros to run tests in isolation.
See below for an example of using ament_add_ros_isolated_gtest with colcon test. All other tests follow a similar pattern.
In my_cool_pkg/test, create the gtest code file test_my_cool_pkg.cpp:
#include \"gtest/gtest.h\"\n#include \"my_cool_pkg/my_cool_pkg.hpp\"\nTEST(TestMyCoolPkg, TestHello) {\nEXPECT_EQ(my_cool_pkg::print_hello(), 0);\n}\nIn package.xml, add the following line:
<test_depend>ament_cmake_ros</test_depend>\nNext add an entry under BUILD_TESTING in the CMakeLists.txt to compile the test source files:
if(BUILD_TESTING)\n\nament_add_ros_isolated_gtest(test_my_cool_pkg test/test_my_cool_pkg.cpp)\ntarget_link_libraries(test_my_cool_pkg ${PROJECT_NAME})\n...\nendif()\nThis automatically links the test with the default main function provided by gtest. The code under test is usually in a different CMake target (${PROJECT_NAME} in the example) and its shared object for linking needs to be added.
To register a new gtest item, wrap the test code with the macro TEST (). TEST () is a predefined macro that helps generate the final test code, and also registers a gtest item to be available for execution. The test case name should be in CamelCase, since gtest inserts an underscore between the fixture name and the class case name when creating the test executable.
gtest/gtest.h also contains predefined macros of gtest like ASSERT_TRUE(condition), ASSERT_FALSE(condition), ASSERT_EQ(val1,val2), ASSERT_STREQ(str1,str2), EXPECT_EQ(), etc. ASSERT_* will abort the test if the condition is not satisfied, while EXPECT_* will mark the test as failed but continue on to the next test condition.
Info
More information about gtest and its features can be found in the gtest repo.
In the demo CMakeLists.txt, ament_add_ros_isolated_gtest is a predefined macro in ament_cmake_ros that helps simplify adding gtest code. Details can be viewed in ament_add_gtest.cmake.
By default, all necessary test files (ELF, CTestTestfile.cmake, etc.) are compiled by colcon:
cd ~/workspace/\ncolcon build --packages-select my_cool_pkg\nTest files are generated under ~/workspace/build/my_cool_pkg.
To run all tests for a specific package, call:
$ colcon test --packages-select my_cool_pkg\n\nStarting >>> my_cool_pkg\nFinished <<< my_cool_pkg [7.80s]\n\nSummary: 1 package finished [9.27s]\nThe test command output contains a brief report of all the test results.
To get job-wise information of all executed tests, call:
$ colcon test-result --all\n\nbuild/my_cool_pkg/test_results/my_cool_pkg/copyright.xunit.xml: 8 tests, 0 errors, 0 failures, 0 skipped\nbuild/my_cool_pkg/test_results/my_cool_pkg/cppcheck.xunit.xml: 6 tests, 0 errors, 0 failures, 0 skipped\nbuild/my_cool_pkg/test_results/my_cool_pkg/lint_cmake.xunit.xml: 1 test, 0 errors, 0 failures, 0 skipped\nbuild/my_cool_pkg/test_results/my_cool_pkg/my_cool_pkg_exe_integration_test.xunit.xml: 1 test, 0 errors, 0 failures, 0 skipped\nbuild/my_cool_pkg/test_results/my_cool_pkg/test_my_cool_pkg.gtest.xml: 1 test, 0 errors, 0 failures, 0 skipped\nbuild/my_cool_pkg/test_results/my_cool_pkg/xmllint.xunit.xml: 1 test, 0 errors, 0 failures, 0 skipped\n\nSummary: 18 tests, 0 errors, 0 failures, 0 skipped\nLook in the ~/workspace/log/test_<date>/<package_name> directory for all the raw test commands, std_out, and std_err. There is also the ~/workspace/log/latest_*/ directory containing symbolic links to the most recent package-level build and test output.
To print the tests' details while the tests are being run, use the --event-handlers console_cohesion+ option to print the details directly to the console:
$ colcon test --event-handlers console_cohesion+ --packages-select my_cool_pkg\n\n...\ntest 1\n Start 1: test_my_cool_pkg\n\n1: Test command: /usr/bin/python3 \"-u\" \"~/workspace/install/share/ament_cmake_test/cmake/run_test.py\" \"~/workspace/build/my_cool_pkg/test_results/my_cool_pkg/test_my_cool_pkg.gtest.xml\" \"--package-name\" \"my_cool_pkg\" \"--output-file\" \"~/workspace/build/my_cool_pkg/ament_cmake_gtest/test_my_cool_pkg.txt\" \"--command\" \"~/workspace/build/my_cool_pkg/test_my_cool_pkg\" \"--gtest_output=xml:~/workspace/build/my_cool_pkg/test_results/my_cool_pkg/test_my_cool_pkg.gtest.xml\"\n1: Test timeout computed to be: 60\n1: -- run_test.py: invoking following command in '~/workspace/src/my_cool_pkg':\n1: - ~/workspace/build/my_cool_pkg/test_my_cool_pkg --gtest_output=xml:~/workspace/build/my_cool_pkg/test_results/my_cool_pkg/test_my_cool_pkg.gtest.xml\n1: [==========] Running 1 test from 1 test case.\n1: [----------] Global test environment set-up.\n1: [----------] 1 test from test_my_cool_pkg\n1: [ RUN ] test_my_cool_pkg.test_hello\n1: Hello World\n1: [ OK ] test_my_cool_pkg.test_hello (0 ms)\n1: [----------] 1 test from test_my_cool_pkg (0 ms total)\n1:\n1: [----------] Global test environment tear-down\n1: [==========] 1 test from 1 test case ran. (0 ms total)\n1: [ PASSED ] 1 test.\n1: -- run_test.py: return code 0\n1: -- run_test.py: inject classname prefix into gtest result file '~/workspace/build/my_cool_pkg/test_results/my_cool_pkg/test_my_cool_pkg.gtest.xml'\n1: -- run_test.py: verify result file '~/workspace/build/my_cool_pkg/test_results/my_cool_pkg/test_my_cool_pkg.gtest.xml'\n1/5 Test #1: test_my_cool_pkg ................... Passed 0.09 sec\n\n...\n\n100% tests passed, 0 tests failed out of 5\n\nLabel Time Summary:\ncopyright = 0.49 sec*proc (1 test)\ncppcheck = 0.20 sec*proc (1 test)\ngtest = 0.05 sec*proc (1 test)\nlint_cmake = 0.18 sec*proc (1 test)\nlinter = 1.34 sec*proc (4 tests)\nxmllint = 0.47 sec*proc (1 test)\n\nTotal Test time (real) = 7.91 sec\n...\nLoosely described, a code coverage metric is a measure of how much of the program code has been exercised (covered) during testing.
In the Autoware repositories, Codecov is used to automatically calculate coverage of any open pull request.
More details about the code coverage metrics can be found in the Codecov documentation.
"},{"location":"datasets/","title":"Datasets","text":""},{"location":"datasets/#datasets","title":"Datasets","text":"Autoware partners provide datasets for testing and development. These datasets are available for download here.
"},{"location":"datasets/#bus-odd-operational-design-domain-datasets","title":"Bus-ODD (Operational Design Domain) datasets","text":""},{"location":"datasets/#leo-drive-isuzu-sensor-data","title":"Leo Drive - ISUZU sensor data","text":"This dataset contains data from the Isuzu bus used in the Bus ODD project.
The data contains data from following sensors:
It also contains /tf topic for static transformations between sensors.
The GNSS data is available in sensor_msgs/msg/NavSatFix message type.
But also the Applanix raw messages are also included in applanix_msgs/msg/NavigationPerformanceGsof50 and applanix_msgs/msg/NavigationSolutionGsof49 message types. In order to be able to play back these messages, you need to build and source the applanix_msgs package.
# Create a workspace and clone the repository\nmkdir -p ~/applanix_ws/src && cd \"$_\"\ngit clone https://github.com/autowarefoundation/applanix.git\ncd ..\n\n# Build the workspace\ncolcon build --symlink-install --packages-select applanix_msgs\n\n# Source the workspace\nsource ~/applanix_ws/install/setup.bash\n\n# Now you can play back the messages\nAlso make sure to source Autoware Universe workspace too.
"},{"location":"datasets/#download-instructions","title":"Download instructions","text":"# Install awscli\n$ sudo apt update && sudo apt install awscli -y\n\n# This will download the entire dataset to the current directory.\n# (About 10.9GB of data)\n$ aws s3 sync s3://autoware-files/collected_data/2022-08-22_leo_drive_isuzu_bags/ ./2022-08-22_leo_drive_isuzu_bags --no-sign-request\n\n# Optionally,\n# If you instead want to download a single bag file, you can get a list of the available files with following:\n$ aws s3 ls s3://autoware-files/collected_data/2022-08-22_leo_drive_isuzu_bags/ --no-sign-request\n PRE all-sensors-bag1_compressed/\n PRE all-sensors-bag2_compressed/\n PRE all-sensors-bag3_compressed/\n PRE all-sensors-bag4_compressed/\n PRE all-sensors-bag5_compressed/\n PRE all-sensors-bag6_compressed/\n PRE driving_20_kmh_2022_06_10-16_01_55_compressed/\n PRE driving_30_kmh_2022_06_10-15_47_42_compressed/\n\n# Then you can download a single bag file with the following:\naws s3 sync s3://autoware-files/collected_data/2022-08-22_leo_drive_isuzu_bags/all-sensors-bag1_compressed/ ./all-sensors-bag1_compressed --no-sign-request\nThis dataset contains pcap files and ros2 bag files from Ouster OS1-64 Lidar. The pcap file and ros2 bag file is recorded in the same time with slight difference in duration.
Click here to download (~553MB)
Reference Issue
"},{"location":"design/","title":"Autoware's Design","text":""},{"location":"design/#autowares-design","title":"Autoware's Design","text":""},{"location":"design/#architecture","title":"Architecture","text":"Core and Universe.
Autoware provides the runtimes and technology components by open-source software. The runtimes are based on the Robot Operating System (ROS). The technology components are provided by contributors, which include, but are not limited to:
The downside of the microautonomy architecture is that the computational performance of end applications is sacrificed due to its data path overhead attributed to functional modularity. In other words, the trade-off characteristic of the microautonomy architecture exists between computational performance and functional modularity. This trade-off problem can be solved technically by introducing real-time capability. This is because autonomous driving systems are not really designed to be real-fast, that is, low-latency computing is nice-to-have but not must-have. The must-have feature for autonomous driving systems is that the latency of computing is predictable, that is, the systems are real-time. As a whole, we can compromise computational performance to an extent that is predictable enough to meet the given timing constraints of autonomous driving systems, often referred to as deadlines of computation.
"},{"location":"design/#design","title":"Design","text":"Warning
Under Construction
"},{"location":"design/#autoware-concepts","title":"Autoware concepts","text":"The Autoware concepts page describes the design philosophy of Autoware. Readers (service providers and all Autoware users) will learn the basic concepts underlying Autoware development, such as microautonomy and the Core/Universe architecture.
"},{"location":"design/#autoware-architecture","title":"Autoware architecture","text":"The Autoware architecture page describes an overview of each module that makes up Autoware. Readers (all Autoware users) will gain a high-level picture of how each module that composes Autoware works.
"},{"location":"design/#autoware-interfaces","title":"Autoware interfaces","text":"The Autoware interfaces page describes in detail the interface of each module that makes up Autoware. Readers (intermediate developers) will learn how to add new functionality to Autoware and how to integrate their own modules with Autoware.
"},{"location":"design/#configuration-management","title":"Configuration management","text":""},{"location":"design/#conclusion","title":"Conclusion","text":""},{"location":"design/autoware-architecture/","title":"Architecture overview","text":""},{"location":"design/autoware-architecture/#architecture-overview","title":"Architecture overview","text":"This page describes the architecture of Autoware.
"},{"location":"design/autoware-architecture/#introduction","title":"Introduction","text":"The current Autoware is defined to be a layered architecture that clarifies each module's role and simplifies the interface between them. By doing so:
Note that the initial focus of this architecture design was solely on driving capability, and so the following features were left as future work:
Autoware's architecture consists of the following six stacks. Each linked page contains a more detailed set of requirements and use cases specific to that stack:
A diagram showing Autoware's nodes in the default configuration can be found on the Node diagram page. Detailed documents for each node are available in the Autoware Universe docs.
Note that Autoware configurations are scalable / selectable and will vary depending on the environment and required use cases.
"},{"location":"design/autoware-architecture/#references","title":"References","text":"This document presents the design concept of the Control Component. The content is as follows:
The Control Component generates the control signal to which the Vehicle Component subscribes. The generated control signals are computed based on the reference trajectories from the Planning Component.
The Control Component consists of two modules. The trajectory_follower module generates a vehicle control command to follow the reference trajectory received from the planning module. The command includes, for example, the desired steering angle and target speed. The vehicle_command_gate is responsible for filtering the control command to prevent abnormal values and then sending it to the vehicle. This gate also allows switching between multiple sources such as the MRM (minimal risk maneuver) module or some remote control module, in addition to the trajectory follower.
The Autoware control system is designed as a platform for automated driving systems that can be compatible with a diverse range of vehicles.
The control process in Autoware uses general information (such as target acceleration and deceleration) and no vehicle-specific information (such as brake pressure) is used. Hence it can be adjusted independently of the vehicle's drive interface enabling easy integration or performance tuning.
Furthermore, significant differences that affect vehicle motion constraints, such as two-wheel steering or four-wheel steering, are addressed by switching the control vehicle model, achieving control specialized for each characteristic.
Autoware's control module outputs the necessary information to control the vehicle as a substitute for a human driver. For example, the control command from the control module looks like the following:
- Target steering angle\n- Target steering torque\n- Target speed\n- Target acceleration\nNote that vehicle-specific values such as pedal positions and low-level information such as individual wheel rotation speeds are excluded from the command.
"},{"location":"design/autoware-architecture/control/#vehicle-adaptation-design","title":"Vehicle Adaptation Design","text":""},{"location":"design/autoware-architecture/control/#vehicle-interface-adapter","title":"Vehicle interface adapter","text":"Autoware is designed to be an autonomous driving platform able to accommodate vehicles with various drivetrain types.
This is an explanation of how Autoware handles the standardization of systems with different vehicle drivetrains. The interfaces for vehicle drivetrains are diverse, including steering angle, steering angular velocity, steering torque, speed, accel/brake pedals, and brake pressure. To accommodate these differences, Autoware adds an adapter module between the control component and the vehicle interface. This module performs the conversion between the proprietary message types used by the vehicle (such as brake pressure) and the generic types used by Autoware (such as desired acceleration). By providing this conversion information, the differences in vehicle drivetrain can be accommodated.
If the information is not known in advance, an automatic calibration tool can be used. Calibration will occur within limited degrees of freedom, generating the information necessary for the drivetrain conversion automatically.
This configuration is summarized in the following diagram.
"},{"location":"design/autoware-architecture/control/#examples-of-several-vehicle-interfaces","title":"Examples of several vehicle interfaces","text":"This is an example of the several drivetrain types in the vehicle interface.
Vehicle Lateral interface Longitudinal interface Note Lexus Steering angle Accel/brake pedal position Acceleration lookup table conversion for longitudinal JPN TAXI Steering angle Accel/brake pedal position Acceleration lookup table conversion for longitudinal GSM8 Steering EPS voltage Acceleration motor voltage, Deceleration brake hydraulic pressure lookup table and PID conversion for lateral and longitudinal YMC Golfcart Steering angle Velocity Logiees yaw rate Velocity F1 TENTH Steering angle Motor RPM interface code"},{"location":"design/autoware-architecture/control/#control-feature-design","title":"Control Feature Design","text":"The following lists the features provided by Autoware's Control/Vehicle component, as well as the conditions and assumptions required to utilize them effectively.
The proper operation of the ODD is limited by factors such as whether the functions are enabled, delay time, calibration accuracy and degradation rate, and sensor accuracy.
Feature Description\u3000 Requirements/Assumptions Note \u3000Limitation for now Lateral Control Control the drivetrain system related to lateral vehicle motion Trying to increase the number of vehicle types that can be supported in the future. Only front-steering type is supported. Longitudinal Control Control the drivetrain system related to longitudinal vehicle motion Slope Compensation Supports precise vehicle motion control on slopes Gradient information can be obtained from maps or sensors attached to the chassis If gradient information is not available, the gradient is estimated from the vehicle's pitch angle. Delay Compensation Controls the drivetrain system appropriately in the presence of time delays The drivetrain delay information is provided in advance If there is no delay information, the drivetrain delay is estimated automatically (automatic calibration). However, the effect of delay cannot be completely eliminated, especially in scenarios with sudden changes in speed. Only fixed delay times can be set for longitudinal and lateral drivetrain systems separately. It does not accommodate different delay times for the accelerator and brake. Drivetrain IF Conversion (Lateral Control) Converts the drivetrain-specific information of the vehicle into the drivetrain information used by Autoware (e.g., target steering angular velocity \u2192 steering torque) The conversion information is provided in advance If there is no conversion information, the conversion map is estimated automatically (automatic calibration). The degree of freedom for conversion is limited (2D lookup table + PID FB). Drivetrain IF Conversion (Longitudinal Control) Converts the drivetrain-specific information of the vehicle into the drivetrain information used by Autoware (e.g., target acceleration \u2192 accelerator/brake pedal value) The conversion information is provided in advance If there is no conversion information, the conversion map is estimated automatically (automatic calibration). The degree of freedom for conversion is limited (2D lookup table + PID FB). Automatic Calibration Automatically estimates and applies values such as drivetrain IF conversion map and delay time. The drivetrain status can be obtained (must) Anomaly Detection Notifies when there is a discrepancy in the calibration or unexpected drivetrain behavior The drivetrain status can be obtained (must) Steering Zero Point Correction Corrects the midpoint of the steering to achieve appropriate steering control The drivetrain status can be obtained (must) Steering Deadzone Correction Corrects the deadzone of the steering to achieve appropriate steering control The steering deadzone parameter is provided in advance If the parameter is unknown, the deadzone parameter is estimated from driving information Not available now Steering Deadzone Estimation Dynamically estimates the steering deadzone from driving data Not available now Weight Compensation Performs appropriate vehicle control according to weight Weight information can be obtained from sensors If there is no weight sensor, estimate the weight from driving information. Currently not available Weight Estimation Dynamically estimates weight from driving data Currently not availableThe list above does not cover wheel control systems such as ABS commonly used in vehicles. Regarding these features, the following considerations are taken into account.
"},{"location":"design/autoware-architecture/control/#integration-with-vehicle-side-functions","title":"Integration with vehicle-side functions","text":"ABS (Anti-lock Brake System) and ESC (Electric Stability Control) are two functions that may be pre-installed on a vehicle, directly impacting its controllability. The control modules of Autoware assume that both ABS and ESC are installed on the vehicle and their absence may cause unreliable controls depending on the target ODD. For example, with low-velocity driving in a controlled environment, these functions are not necessary.
Also, note that this statement does not negate the development of ABS functionality in autonomous driving systems.
"},{"location":"design/autoware-architecture/control/#autoware-capabilities-and-vehicle-requirements","title":"Autoware Capabilities and Vehicle Requirements","text":"As an alternative to human driving, autonomous driving systems essentially aim to handle tasks that humans can perform. This includes not only controlling the steering wheel, accel, and brake, but also automatically detecting issues such as poor brake response or a misaligned steering angle. However, this is a trade-off, as better vehicle performance will lead to superior system behavior, ultimately affecting the design of ODD.
On the other hand, for tasks that are not typically anticipated or cannot be handled by a human driver, processing in the vehicle ECU is expected. Examples of such scenarios include cases where the brake response is clearly delayed or when the vehicle rotates due to a single-side tire slipping. These tasks are typically handled by ABS or ESC.
"},{"location":"design/autoware-architecture/localization/","title":"Index","text":"LOCALIZATION COMPONENT DESIGN DOC
"},{"location":"design/autoware-architecture/localization/#abstract","title":"Abstract","text":""},{"location":"design/autoware-architecture/localization/#1-requirements","title":"1. Requirements","text":"Localization aims to estimate vehicle pose, velocity, and acceleration.
Goals:
Non-goals:
This section shows example sensor configurations and their expected performances. Each sensor has its own advantages and disadvantages, but overall performance can be improved by fusing multiple sensors.
"},{"location":"design/autoware-architecture/localization/#3d-lidar-pointcloud-map","title":"3D-LiDAR + PointCloud Map","text":""},{"location":"design/autoware-architecture/localization/#expected-situation","title":"Expected situation","text":"Two architectures are defined, \"Required\" and \"Recommended\". However, the \"Required\" architecture only contains the inputs and outputs necessary to accept various localization algorithms. To improve the reusability of each module, the required components are defined in the \"Recommended\" architecture section along with a more detailed explanation.
"},{"location":"design/autoware-architecture/localization/#required-architecture","title":"Required Architecture","text":""},{"location":"design/autoware-architecture/localization/#input","title":"Input","text":"PoseTwistFusionFilterDevelopers can optionally add other frames such as odom or base_footprint as long as the tf structure above is maintained.
"},{"location":"design/autoware-architecture/localization/#the-localization-modules-ideal-functionality","title":"The localization module's ideal functionality","text":"To maintain sufficient pose estimation performance for safe operation, the following metrics are considered:
For more details about bias, refer to the VectorNav IMU specifications page.\u00a0\u21a9
Autoware relies on high-definition point cloud maps and vector maps of the driving environment to perform various tasks such as localization, route planning, traffic light detection, and predicting the trajectories of pedestrians and other vehicles.
This document describes the design of map component of Autoware, including its requirements, architecture design, features, data formats, and interface to distribute map information to the rest of autonomous driving stack.
"},{"location":"design/autoware-architecture/map/#2-requirements","title":"2. Requirements","text":"Map should provide two types of information to the rest of the stack:
A vector map contains highly accurate information about a road network, lane geometry, and traffic lights. It is required for route planning, traffic light detection, and predicting the trajectories of other vehicles and pedestrians.
A 3D point cloud map is primarily used for LiDAR-based localization and part of perception in Autoware. In order to determine the current position and orientation of the vehicle, a live scan captured from one or more LiDAR units is matched against a pre-generated 3D point cloud map. Therefore, an accurate point cloud map is crucial for good localization results. However, if the vehicle has an alternate localization method with enough accuracy, for example using camera-based localization, point cloud map may not be required to use Autoware.
"},{"location":"design/autoware-architecture/map/#3-architecture","title":"3. Architecture","text":"Warning
Under Construction
"},{"location":"design/autoware-architecture/map/#4-features","title":"4. Features","text":"Warning
Under Construction
"},{"location":"design/autoware-architecture/map/#5-map-specification","title":"5. Map Specification","text":""},{"location":"design/autoware-architecture/map/#point-cloud-map","title":"Point Cloud Map","text":"The point cloud map must be supplied as a file with the following requirements:
Note
Three global coordinate systems are currently supported by Autoware, including Military Grid Reference System (MGRS), Universal Transverse Mercator (UTM), and Japan Rectangular Coordinate System. However, MGRS is a preferred coordinate system for georeferenced maps. In a map with MGRS coordinate system, the X and Y coordinates of each point represent the point's location within the 100,000-meter square, while the Z coordinate represents the point's elevation.
If it is split into a single file, Autoware assumes the following directory structure by default.
sample-map-rosbag\n\u251c\u2500\u2500 lanelet2_map.osm\n\u251c\u2500\u2500 pointcloud_map.pcd\nIf it is split into multiple files, Autoware assumes the following directory structure by default.
sample-map-rosbag\n\u251c\u2500\u2500 lanelet2_map.osm\n\u251c\u2500\u2500 pointcloud_map\n\u251c\u2500\u2500 pcd_00.pcd\n\u251c\u2500\u2500 pcd_01.pcd\n\u251c\u2500\u2500 pcd_02.pcd\n\u251c\u2500\u2500 ...\n\u2514\u2500\u2500 pointcloud_map_metadata.yaml\nNote that, if you split the map into multiple files, you must meet the following additional conditions:
Metadata should look like as follows:
x_resolution: 100.0\ny_resolution: 150.0\nA.pcd: [1200, 2500] # -> 1200 < x < 1300, 2500 < y < 2650\nB.pcd: [1300, 2500] # -> 1300 < x < 1400, 2500 < y < 2650\nC.pcd: [1200, 2650] # -> 1200 < x < 1300, 2650 < y < 2800\nD.pcd: [1400, 2650] # -> 1400 < x < 1500, 2650 < y < 2800\nYou may use pointcloud_divider from MAP IV for dividing pointcloud map as well as generating the compatible metadata.yaml.
"},{"location":"design/autoware-architecture/map/#vector-map","title":"Vector Map","text":"The vector cloud map must be supplied as a file with the following requirements:
Warning
Under Construction
"},{"location":"design/autoware-architecture/node-diagram/","title":"Node diagram","text":""},{"location":"design/autoware-architecture/node-diagram/#node-diagram","title":"Node diagram","text":"This page depicts the node diagram designs for Autoware Core/Universe architecture.
"},{"location":"design/autoware-architecture/node-diagram/#autoware-core","title":"Autoware Core","text":"TBD.
"},{"location":"design/autoware-architecture/node-diagram/#autoware-universe","title":"Autoware Universe","text":"Open in draw.io for fullscreen
Note that the diagram is for reference. We are planning to update this diagram every release and may have old information between the releases. If you wish to check the latest node diagram use rqt_graph after launching the Autoware.
"},{"location":"design/autoware-architecture/perception/","title":"Perception Component design","text":""},{"location":"design/autoware-architecture/perception/#perception-component-design","title":"Perception Component design","text":""},{"location":"design/autoware-architecture/perception/#purpose-of-this-document","title":"Purpose of this document","text":"This document outlines the high-level design strategies, goals and related rationales in the development of the Perception Component. Through this document, it is expected that all OSS developers will comprehend the design philosophy, goals and constraints under which the Perception Component is designed, and participate seamlessly in the development.
"},{"location":"design/autoware-architecture/perception/#overview","title":"Overview","text":"The Perception Component receives inputs from Sensing, Localization, and Map components, and adds semantic information (e.g., Object Recognition, Obstacle Segmentation, Traffic Light Recognition, Occupancy Grid Map), which is then passed on to Planning Component. This component design follows the overarching philosophy of Autoware, defined as the microautonomy concept.
"},{"location":"design/autoware-architecture/perception/#goals-and-non-goals","title":"Goals and non-goals","text":"The role of the Perception Component is to recognize the surrounding environment based on the data obtained through Sensing and acquire sufficient information (such as the presence of dynamic objects, stationary obstacles, blind spots, and traffic signal information) to enable autonomous driving.
In our overall design, we emphasize the concept of microautonomy architecture. This term refers to a design approach that focuses on the proper modularization of functions, clear definition of interfaces between these modules, and as a result, high expandability of the system. Given this context, the goal of the Perception Component is set not to solve every conceivable complex use case (although we do aim to support basic ones), but rather to provide a platform that can be customized to the user's needs and can facilitate the development of additional features.
To clarify the design concepts, the following points are listed as goals and non-goals.
Goals:
Non-goals:
This diagram describes the high-level architecture of the Perception Component.
The Perception Component consists of the following sub-components:
The following describes the input/output concept between Perception Component and other components. See the Perception Component Interface (WIP) page for the current implementation.
"},{"location":"design/autoware-architecture/perception/#input-to-the-perception-component","title":"Input to the Perception Component","text":"As mentioned in the goal session, this perception module is designed to be extensible by third-party components. For specific instructions on how to add new modules and expand its functionality, please refer to the provided documentation or guidelines (WIP).
"},{"location":"design/autoware-architecture/perception/#supported-functions","title":"Supported Functions","text":"Feature Description Requirements LiDAR DNN based 3D detector This module takes point clouds as input and performs detection of objects such as vehicles, trucks, buses, pedestrians, and bicycles. - Point Clouds Camera DNN based 2D detector This module takes camera image as input and performs detection of objects such as vehicles, trucks, buses, pedestrians, and bicycles. - Camera Images LiDAR Clustering This module performs clustering of point clouds and shape estimation to achieve object detection without labels. - Point Clouds Semi-rule based detector This module performs object detection using information from both images and point clouds, and it consists of two components: LiDAR Clustering and Camera DNN based 2D detector. - Output from Camera DNN based 2D detector and LiDAR Clustering Object Merger This module integrates results from various detectors. - Detected Objects Interpolator This module stabilizes the object detection results by maintaining long-term detection results using Tracking results. - Detected Objects - Tracked Objects Tracking This module gives ID and estimate velocity to the detection results. - Detected Objects Prediction This module predicts the future paths (and their probabilities) of dynamic objects according to the shape of the map and the surrounding environment. - Tracked Objects - Vector Map Obstacle Segmentation This module identifies point clouds originating from obstacles that the ego vehicle should avoid. - Point Clouds - Point Cloud Map Occupancy Grid Map This module detects blind spots (areas where no information is available and where dynamic objects may jump out). - Point Clouds - Point Cloud Map Traffic Light Recognition This module detects the position and state of traffic signals. - Camera Images - Vector Map"},{"location":"design/autoware-architecture/perception/#reference-implementation","title":"Reference Implementation","text":"When Autoware is launched, the default parameters are loaded, and the Reference Implementation is started. For more details, please refer to the Reference Implementation.
"},{"location":"design/autoware-architecture/perception/reference_implementation/","title":"Perception Component Reference Implementation Design","text":""},{"location":"design/autoware-architecture/perception/reference_implementation/#perception-component-reference-implementation-design","title":"Perception Component Reference Implementation Design","text":""},{"location":"design/autoware-architecture/perception/reference_implementation/#purpose-of-this-document","title":"Purpose of this document","text":"This document outlines detailed design of the reference imprementations. This allows developers and users to understand what is currently available with the Perception Component, how to utilize, expand, or add to its features.
"},{"location":"design/autoware-architecture/perception/reference_implementation/#architecture","title":"Architecture","text":"This diagram describes the architecture of the reference implementation.
The Perception component consists of the following sub-components:
The Planning component generates the trajectory message that will be subscribed to by the Control component based on the environmental state obtained from the Localization and the Perception components.
"},{"location":"design/autoware-architecture/planning/#requirements","title":"Requirements","text":"The goal of the Planning component is to generate a trajectory (path and velocity) of the ego vehicle that is safe and well-regulated while satisfying the given mission.
Goals:
Non-goals:
This diagram describes the high-level architecture of the Planning Component.
The Planning component consists of the following sub-components:
Each component contains some modules that can be dynamically loaded and unloaded based on the situation. For instance, the Behavior Planning component includes modules such as lane change, intersection, and crosswalk modules.
Our planning components are built based on the microautonomy architecture with Autoware. We adopt a modular system framework where the tasks are implemented as modules that can be dynamically loaded and unloaded to achieve different features depending on the given use cases.
"},{"location":"design/autoware-architecture/planning/#component-interface","title":"Component interface","text":"The following describes the input/output concept between Planning Component and other components. See the Planning Component Interface (WIP) page for the current implementation.
"},{"location":"design/autoware-architecture/planning/#input-to-the-planning-component","title":"Input to the planning component","text":"As mentioned in the goal session, this planning module is designed to be extensible by third-party components. For specific instructions on how to add new modules and expand its functionality, please refer to the provided documentation or guidelines (WIP).
"},{"location":"design/autoware-architecture/planning/#supported-functions","title":"Supported Functions","text":"Feature Description Requirements Figure Route Planning Plan route from the ego vehicle position to the destination. Reference implementation is in Mission Planner, enabled by launching themission_planner node. - Lanelet map (driving lanelets) Path Planning from Route Plan path to be followed from the given route. Reference implementation is in Behavior Path Planner. - Lanelet map (driving lanelets) Obstacle Avoidance Plan path to avoid obstacles by steering operation. Reference implementation is in Avoidance, Obstacle Avoidance Planner. Enable flag in parameter: launch obstacle_avoidance_planner true - objects information Path Smoothing Plan path to achieve smooth steering. Reference implementation is in Obstacle Avoidance Planner. - Lanelet map (driving lanelet) Narrow Space Driving Plan path to drive within the drivable area. Furthermore, when it is not possible to drive within the drivable area, stop the vehicle to avoid exiting the drivable area. Reference implementation is in Obstacle Avoidance Planner. - Lanelet map (high-precision lane boundaries) Lane Change Plan path for lane change to reach the destination. Reference implementation is in Lane Change.. Enable flag in both parameters: - Lanelet map (driving lanelets) Pull Over Plan path for pull over to park at the road shoulder. Reference implementation is in Goal Planner. - Lanelet map (shoulder lane) Pull Out Plan path for pull over to start from the road shoulder. Reference implementation is in Pull Out Module. - Lanelet map (shoulder lane) Path Shift Plan path in lateral direction in response to external instructions. Reference implementation is in Side Shift Module. - None Obstacle Stop Plan velocity to stop for an obstacle on the path. Reference implementation is in Obstacle Stop Planner, Obstacle Cruise Planner. launch obstacle_stop_planner and enable flag: TODO, launch obstacle_cruise_planner and enable flag: TODO - objects information Obstacle Deceleration Plan velocity to decelerate for an obstacle located around the path. Reference implementation is in Obstacle Stop Planner, Obstacle Cruise Planner. - objects information Adaptive Cruise Control Plan velocity to follow the vehicle driving in front of the ego vehicle. Reference implementation is in Obstacle Stop Planner, Obstacle Cruise Planner. - objects information Decelerate for cut-in vehicles Plan velocity to avoid a risk for cutting-in vehicle to ego lane. Reference implementation is in Obstacle Cruise Planner. - objects information Surround Check at starting Plan velocity to prevent moving when an obstacle exists around the vehicle. Reference implementation is in Surround Obstacle Checker. - objects information Curve Deceleration Plan velocity to decelerate the speed on a curve. Reference implementation is in Motion Velocity Smoother. - None Curve Deceleration for Obstacle Plan velocity to decelerate the speed on a curve for a risk of obstacle collision around the path. Reference implementation is in Obstacle Velocity Limiter. - objects information - Lanelet map (static obstacle) Crosswalk Plan velocity to stop or decelerate for pedestrians approaching or walking on a crosswalk. Reference implementation is in Crosswalk Module. - objects information - Lanelet map (pedestrian crossing) Intersection Oncoming Vehicle Check Plan velocity for turning right/left at intersection to avoid a risk with oncoming other vehicles. Reference implementation is in Intersection Module. - objects information - Lanelet map (intersection lane and yield lane) Intersection Blind Spot Check Plan velocity for turning right/left at intersection to avoid a risk with other vehicles or motorcycles coming from behind blind spot. Reference implementation is in Intersection Module. - objects information - Lanelet map (intersection lane) Intersection Occlusion Check Plan velocity for turning right/left at intersection to avoid a risk with the possibility of coming vehicles from occlusion area. Reference implementation is in Intersection Module. - objects information - Lanelet map (intersection lane) WIP Intersection Traffic Jam Detection Plan velocity for intersection not to enter the intersection when a vehicle is stopped ahead for a traffic jam. Reference implementation is in Intersection Module. - objects information - Lanelet map (intersection lane) Traffic Light Plan velocity for intersection according to a traffic light signal. Reference implementation is in Traffic Light Module. - Traffic light color information Run-out Check Plan velocity to decelerate for the possibility of nearby objects running out into the path. Reference implementation is in Run Out Module. - objects information Stop Line Plan velocity to stop at a stop line. Reference implementation is in Stop Line Module. - Lanelet map (stop line) Occlusion Spot Check Plan velocity to decelerate for objects running out from occlusion area, for example, from behind a large vehicle. Reference implementation is in Occlusion Spot Module. - objects information - Lanelet map (private/public lane) No Stop Area Plan velocity not to stop in areas where stopping is prohibited, such as in front of the fire station entrance. Reference implementation is in No Stopping Area Module. - Lanelet map (no stopping area) Merge from Private Area to Public Road Plan velocity for entering the public road from a private driveway to avoid a risk of collision with pedestrians or other vehicles. Reference implementation is in Merge from Private Area Module. - objects information - Lanelet map (private/public lane) WIP Speed Bump Plan velocity to decelerate for speed bumps. Reference implementation is in Speed Bump Module. - Lanelet map (speed bump) WIP Detection Area Plan velocity to stop at the corresponding stop when an object exist in the designated detection area. Reference implementation is in Detection Area Module. - Lanelet map (detection area) Out of ODD area Plan velocity to stop before exiting the area designated by ODD (Operational Design Domain). Reference implementation is in (WIP). - Lanelet map (invalid lanelet) WIP Collision Detection when deviating from lane Plan velocity to avoid conflict with other vehicles driving in the another lane when the ego vehicle is deviating from own lane. Reference implementation is in Out of Lane Module. - objects information - Lanelet map (driving lane) WIP Parking Plan path and velocity for given goal in parking area. Reference implementation is in Free Space Planner. - objects information - Lanelet map (parking area) Autonomous Emergency Braking (AEB) Perform an emergency stop if a collision with an object ahead is anticipated. It is noted that this function is expected as a final safety layer, and this should work even in the event of failures in the Localization or Perception system. Reference implementation is in Out of Lane Module. - Primitive objects WIP Minimum Risk Maneuver (MRM) Provide appropriate MRM (Minimum Risk Maneuver) instructions when a hazardous event occurs. For example, when a sensor trouble found, send an instruction for emergency braking, moderate stop, or pulling over to the shoulder, depending on the severity of the situation. Reference implementation is in TODO - TODO WIP Trajectory Validation Check the planned trajectory is safe. If it is unsafe, take appropriate action, such as modify the trajectory, stop sending the trajectory or report to the autonomous driving system. Reference implementation is in Planning Validator. - None WIP Running Lane Map Generation Generate lane map from localization data recorded in manual driving. Reference implementation is in WIP - None WIP Running Lane Optimization Optimize the centerline (reference path) of the map to make it smooth considering the vehicle kinematics. Reference implementation is in Static Centerline Optimizer. - Lanelet map (driving lanes) WIP"},{"location":"design/autoware-architecture/planning/#reference-implementation","title":"Reference Implementation","text":"The following diagram describes the reference implementation of the Planning component. By adding new modules or extending the functionalities, various ODDs can be supported.
Note that some implementation does not adhere to the high-level architecture design and require updating.
For more details, please refer to the design documents in each package.
obstacle_stop_planner stop_planner.stop_position.max_longitudinal_margin double distance between the ego and the front vehicle when stopping (when cruise_planner_type:=obstacle_stop_planner) obstacle_cruise_planner common.safe_distance_margin double distance between the ego and the front vehicle when stopping (when cruise_planner_type:=obstacle_cruise_planner) behavior_path_planner avoidance.avoidance.lateral.lateral_collision_margin double minimum lateral margin to obstacle on avoidance behavior_path_planner avoidance.avoidance.lateral.lateral_collision_safety_buffer double additional lateral margin to obstacle if possible on avoidance obstacle_avoidance_planner option.enable_outside_drivable_area_stop bool If set true, a stop point will be inserted before the path footprint is outside the drivable area."},{"location":"design/autoware-architecture/planning/#notation","title":"Notation","text":""},{"location":"design/autoware-architecture/planning/#1-self-crossing-road-and-overlapped","title":"[1] self-crossing road and overlapped","text":"To support the self-crossing road and overlapped road in the opposite direction, each planning module has to meet the specifications
Currently, the supported modules are as follows.
Some functions do not support paths with only one point. Therefore, each modules should generate the path with more than two path points.
"},{"location":"design/autoware-architecture/sensing/","title":"Sensing component design","text":""},{"location":"design/autoware-architecture/sensing/#sensing-component-design","title":"Sensing component design","text":""},{"location":"design/autoware-architecture/sensing/#overview","title":"Overview","text":"Sensing component is a collection of modules that apply some primitive pre-processing to the raw sensor data.
The sensor input formats are defined in this component.
"},{"location":"design/autoware-architecture/sensing/#role","title":"Role","text":"Warning
Under Construction
"},{"location":"design/autoware-architecture/sensing/data-types/image/","title":"Image pre-processing design","text":""},{"location":"design/autoware-architecture/sensing/data-types/image/#image-pre-processing-design","title":"Image pre-processing design","text":"Warning
Under Construction
"},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/","title":"Point cloud pre-processing design","text":""},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/#point-cloud-pre-processing-design","title":"Point cloud pre-processing design","text":""},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/#overview","title":"Overview","text":"Point cloud pre-processing is a collection of modules that apply some primitive pre-processing to the raw sensor data.
This pipeline covers the flow of data from drivers to the perception stack.
"},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/#recommended-processing-pipeline","title":"Recommended processing pipeline","text":"graph TD\n Driver[\"Lidar Driver\"] -->|\"Cloud XYZIRCADT\"| FilterPR[\"Polygon Remover Filter / CropBox Filter\"]\n\n subgraph \"sensing\"\n FilterPR -->|\"Cloud XYZIRCADT\"| FilterDC[\"Motion Distortion Corrector Filter\"]\n FilterDC -->|\"Cloud XYZIRCAD\"| FilterOF[\"Outlier Remover Filter\"]\n FilterOF -->|\"Cloud XYZIRC\"| FilterDS[\"Downsampler Filter\"]\n FilterDS -->|\"Cloud XYZIRC\"| FilterTrans[\"Cloud Transformer\"]\n FilterTrans -->|\"Cloud XYZIRC\"| FilterC\n\n FilterX[\"...\"] -->|\"Cloud XYZIRC (i)\"| FilterC[\"Cloud Concatenator\"]\n end\n\n FilterC -->|\"Cloud XYZIRC\"| SegGr[\"Ground Segmentation\"]The modules used here are from pointcloud_preprocessor package.
For details about the modules, see the following table.
It is recommended that these modules are used in a single container as components. For details see ROS 2 Composition
"},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/#point-cloud-fields","title":"Point cloud fields","text":"In the ideal case, the driver is expected to output a point cloud with the PointXYZIRCADT point type.
X FLOAT32 false X position Y FLOAT32 false Y position Z FLOAT32 false Z position I (intensity) UINT8 false Measured reflectivity, intensity of the point R (return type) UINT8 false Laser return type for dual return lidars C (channel) UINT16 false Vertical channel id of the laser that measured the point A (azimuth) FLOAT32 true atan2(Y, X), Horizontal angle from the front of the lidar to the point D (distance) FLOAT32 true hypot(X, Y, Z), Euclidean distance of the point to lidar T (time) UINT32 false Nanoseconds passed since the time of the header when this point was measured Note
A (azimuth) and D (distance) fields are derived fields. They are provided by the driver to reduce the computational load on some parts of the perception stack.
Note
If the Motion Distortion Corrector Filter won't be used, the T (time) field can be omitted, PointXYZIRCAD point type can be used.
Warning
Autoware will support conversion from PointXYZI to PointXYZIRC or PointXYZIRCAD (with channel and return is set to 0) for prototyping purposes. However, this conversion is not recommended for production use since it's not efficient.
We will use following ranges for intensity, compatible with the VLP16 User Manual:
Quoting from the VLP-16 User Manual:
For each laser measurement, a reflectivity byte is returned in addition to distance. Reflectivity byte values are segmented into two ranges, allowing software to distinguish diffuse reflectors (e.g. tree trunks, clothing) in the low range from retroreflectors (e.g. road signs, license plates) in the high range. A retroreflector reflects light back to its source with a minimum of scattering. The VLP-16 provides its own light, with negligible separation between transmitting laser and receiving detector, so retroreflecting surfaces pop with reflected IR light compared to diffuse reflectors that tend to scatter reflected energy.
In a typical point cloud without retroreflectors, all intensity points will be between 0 and 100.
Retroreflective Gradient road sign, Image Source
But in a point cloud with retroreflectors, the intensity points will be between 0 and 255.
"},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/#intensity-mapping-for-other-lidar-brands","title":"Intensity mapping for other lidar brands","text":""},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/#hesai-pandarxt16","title":"Hesai PandarXT16","text":"Hesai Pandar XT16 User Manual
This lidar has 2 modes for reporting reflectivity:
If you are using linear mapping mode, you should map from [0, 255] to [0, 100] when constructing the point cloud.
If you are using non-linear mapping mode, you should map (hesai to autoware)
when constructing the point cloud.
"},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/#livox-mid-70","title":"Livox Mid-70","text":"Livox Mid-70 User Manual
This lidar has 2 modes for reporting reflectivity similar to Velodyne VLP-16, only the ranges are slightly different.
You should map (livox to autoware)
when constructing the point cloud.
"},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/#robosense-rs-lidar-16","title":"RoboSense RS-LiDAR-16","text":"RoboSense RS-LiDAR-16 User Manual
No mapping required, same as Velodyne VLP-16.
"},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/#ouster-os-1-64","title":"Ouster OS-1-64","text":"Software User Manual v2.0.0 for all Ouster sensors
In the manual it is stated:
Reflectivity [16 bit unsigned int] - sensor Signal Photons measurements are scaled based on measured range and sensor sensitivity at that range, providing an indication of target reflectivity. Calibration of this measurement has not currently been rigorously implemented, but this will be updated in a future firmware release.
So it is advised to map the 16 bit reflectivity to [0, 100] range.
"},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/#leishen-ch64w","title":"Leishen CH64W","text":"I couldn't get the english user manual, link of website
In a user manual I was able to find it says:
Byte 7 represents echo strength, and the value range is 0-255. (Echo strength can reflect the energy reflection characteristics of the measured object in the actual measurement environment. Therefore, the echo strength can be used to distinguish objects with different reflection characteristics.)
So it is advised to map the [0, 255] to [0, 100] range.
"},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/#return-type","title":"Return type","text":"Various lidars support multiple return modes. Velodyne lidars support Strongest and Last return modes.
In the PointXYZIRCT and PointXYZIRC types, R field represents return mode with an UINT8.
0 Unknown / Not Marked 1 Strongest 2 Last"},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/#channel","title":"Channel","text":"The channel field is used to identify the vertical channel of the laser that measured the point. In various lidar manuals or literature, it can also be called laser id, ring, laser line.
For Velodyne VLP-16, there are 16 channels. Default order of channels in drivers are generally in firing order.
In the PointXYZIRCT and PointXYZIRC types, C field represents the vertical channel id with an UINT16.
Warning
This section is subject to change. Following are suggestions and open for discussion.
For solid state lidars that have lines, assign row number as the channel id.
For petal pattern lidars, you can keep channel 0.
"},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/#time","title":"Time","text":"In lidar point clouds, each point measurement can have its individual time stamp. This information can be used to eliminate the motion blur that is caused by the movement of the lidar during the scan.
"},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/#point-cloud-header-time","title":"Point cloud header time","text":"The header contains a Time field. The time field has 2 components:
Field Type Descriptionsec int32 Unix time (seconds elapsed since January 1, 1970) nanosec uint32 Nanoseconds elapsed since the sec field The header of the point cloud message is expected to have the time of the earliest point it has.
Note
The sec field is int32 in ROS 2 humble. The largest value it can represent is 2^31 seconds, it is subject to year 2038 problems. We will wait for actions on ROS 2 community side.
More info at: https://github.com/ros2/rcl_interfaces/issues/85
"},{"location":"design/autoware-architecture/sensing/data-types/point-cloud/#individual-point-time","title":"Individual point time","text":"Each PointXYZIRCT point type has the T field for representing the nanoseconds passed since the first-shot point of the point cloud.
To calculate exact time each point was shot, the T nanoseconds are added to the header time.
Note
The T field is uint32 type. The largest value it can represent is 2^32 nanoseconds, which equates to roughly 4.29 seconds. Usual point clouds don't last more than 100ms for full cycle. So this field should be enough.
Warning
Under Construction
"},{"location":"design/autoware-architecture/sensing/data-types/ultrasonics-data/","title":"Ultrasonics data pre-processing design","text":""},{"location":"design/autoware-architecture/sensing/data-types/ultrasonics-data/#ultrasonics-data-pre-processing-design","title":"Ultrasonics data pre-processing design","text":"Warning
Under Construction
"},{"location":"design/autoware-architecture/vehicle/","title":"Vehicle Interface design","text":""},{"location":"design/autoware-architecture/vehicle/#vehicle-interface-design","title":"Vehicle Interface design","text":""},{"location":"design/autoware-architecture/vehicle/#abstract","title":"Abstract","text":"The Vehicle Interface component provides an interface between Autoware and a vehicle that passes control signals to the vehicle\u2019s drive-by-wire system and receives vehicle information that is passed back to Autoware.
"},{"location":"design/autoware-architecture/vehicle/#1-requirements","title":"1. Requirements","text":"Goals:
Non-goals:
The Vehicle Interface component consists of the following components:
Each component contains static nodes of Autoware, while each module can be dynamically loaded and unloaded (corresponding to C++ classes). The mechanism of the Vehicle Interface component is depicted by the following figures:
"},{"location":"design/autoware-architecture/vehicle/#3-features","title":"3. Features","text":"The Vehicle Interface component can provide the following features in functionality and capability:
Basic functions
Additional functionality and capability features may be added, depending on the vehicle hardware. Some example features are listed below:
The interface of the Vehicle Interface component for other components running in the same process space to access the functionality and capability of the Vehicle Interface component is defined as follows.
From Control
From Planning
From the vehicle
The output interface of the Vehicle Interface component:
The data structure for the internal representation of semantics for the objects and trajectories used in the Vehicle Interface component is defined as follows:
"},{"location":"design/autoware-architecture/vehicle/#5-concerns-assumptions-and-limitations","title":"5. Concerns, Assumptions, and Limitations","text":"Concerns
Assumptions
-
Limitations
"},{"location":"design/autoware-architecture/vehicle/#6-examples-of-accuracy-requirements-by-odd","title":"6. Examples of accuracy requirements by ODD","text":""},{"location":"design/autoware-concepts/","title":"Autoware concepts","text":""},{"location":"design/autoware-concepts/#autoware-concepts","title":"Autoware concepts","text":"Autoware is the world\u2019s first open-source software for autonomous driving systems. Autoware provides value for both The technology developers of autonomous driving systems can create new components based on Autoware. The service operators of autonomous driving systems, on the other hand, can select appropriate technology components with Autoware. This is enabled by the microautonomy architecture that modularizes its software stack into the core and universe subsystems (modules).
"},{"location":"design/autoware-concepts/#microautonomy-architecture","title":"Microautonomy architecture","text":"Autoware uses a pipeline architecture to enable the development of autonomous driving systems. The pipeline architecture used in Autoware consists of components similar to three-layer-architecture. And they run in parallel. There are 2 main modules: the Core and the Universe. The components in these modules are designed to be extensible and reusable. And we call it microautonomy architecture.
"},{"location":"design/autoware-concepts/#the-core-module","title":"The Core module","text":"The Core module contains basic runtimes and technology components that satisfy the basic functionality and capability of sensing, computing, and actuation required for autonomous driving systems. AWF develops and maintains the Core module with their architects and leading members through their working groups. Anyone can contribute to the Core but the PR(Pull Request) acceptance criteria is more strict compared to the Universe.
"},{"location":"design/autoware-concepts/#the-universe-module","title":"The Universe module","text":"The Universe modules are extensions to the Core module that can be provided by the technology developers to enhance the functionality and capability of sensing, computing, and actuation. AWF provides the base Universe module to extend from. A key feature of the microautonomy architecture is that the Universe modules can be contributed to by any organization and individual. That is, you can even create your Universe and make it available for the Autoware community and ecosystem. AWF is responsible for quality control of the Universe modules through their development process. As a result, there are multiple types of the Universe modules - some are verified and validated by AWF and others are not. It is up to the users of Autoware which Universe modules are selected and integrated to build their end applications.
"},{"location":"design/autoware-concepts/#interface-design","title":"Interface design","text":"The interface design is the most essential piece of the microautonomy architecture, which is classified into internal and external interfaces. The component interface is designed for the components in a Universe module to communicate with those in other modules, including the Core module, within Autoware internally. The AD(Autonomous Driving) API, on the other hand, is designed for the applications of Autoware to access the technology components in the Core and Universe modules of Autoware externally. Designing solid interfaces, the microautonomy architecture is made possible with AWF's partners, and at the same time is made feasible for the partners.
"},{"location":"design/autoware-concepts/#challenges","title":"Challenges","text":"A grand challenge of the microautonomy architecture is to achieve real-time capability, which guarantees all the technology components activated in the system to predictably meet timing constraints (given deadlines). In general, it is difficult, if not impossible, to tightly estimate the worst-case execution times (WCETs) of components.
In addition, it is also difficult, if not impossible, to tightly estimate the end-to-end latency of components connected by a DAG. Autonomous driving systems based on the microautonomy architecture, therefore, must be designed to be fail-safe but not never-fail. We accept that the timing constraints may be violated (the given deadlines may be missed) as far as the overrun is taken into account. The overrun handlers are two-fold: (i) platform-defined and (ii) user-defined. The platform-defined handler is implemented as part of the platform by default, while the user-defined handler can overwrite it or add a new handler to the system. This is what we call \u201cfail-safe\u201d on a timely basis.
"},{"location":"design/autoware-concepts/#requirements-and-roadmap","title":"Requirements and roadmap","text":"Goals:
Autoware is the world's first \"all-in-one\" open-source software for self-driving vehicles. Since it was first released in 2015, there have been multiple releases made with differing underlying concepts, each one aimed at improving the software.
"},{"location":"design/autoware-concepts/difference-from-ai-and-auto/#autowareai","title":"Autoware.AI","text":"Autoware.AI is the first distribution of Autoware that was released based on ROS 1. The repository contains a variety of packages covering different aspects of autonomous driving technologies - sensing, actuation, localization, mapping, perception and planning.
While it was successful in attracting many developers and contributions, it was difficult to improve Autoware.AI's capabilities for a number of reasons:
Furthermore, there was no clear definition of the conditions under which an Autoware-enabled autonomous vehicle could operate, nor of the use cases or situations supported (eg: the ability to overtake a stationary vehicle).
From the lessons learned from Autoware.AI development, a different development process was taken for Autoware.Auto to develop a ROS 2 version of Autoware.
Warning
Autoware.AI is currently in maintenance mode and will reach end-of-life at the end of 2022.
"},{"location":"design/autoware-concepts/difference-from-ai-and-auto/#autowareauto","title":"Autoware.Auto","text":"Autoware.Auto is the second distribution of Autoware that was released based on ROS 2. As part of the transition to ROS 2, it was decided to avoid simply porting Autoware.AI from ROS 1 to ROS 2. Instead, the codebase was rewritten from scratch with proper engineering practices, including defining target use cases and ODDs (eg: Autonomous Valet Parking [AVP], Cargo Delivery, etc.), designing a proper architecture, writing design documents and test code.
Autoware.Auto development seemed to work fine initially, but after completing the AVP and and Cargo Delivery ODD projects, we started to see the following issues:
In order to address the issues with Autoware.Auto development, the Autoware Foundation decided to create a new architecture called Autoware Core/Universe.
Autoware Core carries over the original policy of Autoware.Auto to be a stable and well-tested codebase. Alongside Autoware Core is a new concept called Autoware Universe, which acts as an extension of Autoware Core with the following benefits:
This way, the primary requirement of having a stable and safe autonomous driving system can be achieved, whilst simultaneously enabling access to state-of-the-art features created by third-party contributors. For more details about the design of Autoware Core/Universe, refer to the Autoware concepts documentation page.
"},{"location":"design/autoware-interfaces/","title":"Autoware interface design","text":""},{"location":"design/autoware-interfaces/#autoware-interface-design","title":"Autoware interface design","text":""},{"location":"design/autoware-interfaces/#abstract","title":"Abstract","text":"Autoware defines three categories of interfaces. The first one is Autoware AD API for operating the vehicle from outside the autonomous driving system such as the Fleet Management System (FMS) and Human Machine Interface (HMI) for operators or passengers. The second one is Autoware component interface for components to communicate with each other. The last one is the local interface used inside the component.
"},{"location":"design/autoware-interfaces/#concept","title":"Concept","text":"Applications can operate multiple and various vehicles in a common way.
Applications are not affected by version updates and implementation changes.
Developers only need to know the interface to add new features and hardware.
Goals:
Non-goals:
The components of Autoware are connected via the component interface. Each component uses the interface to provide functionality and to access other components. AD API implementation is also a component. Since the functional elements required for AD API are defined as the component interface, other components do not need to consider AD API directly. Tools for evaluation and debugging, such as simulators, access both AD API and the component interface.
The component interface has a hierarchical specification. The top-level architecture consists of some components. Each component has some options of the next-level architecture. Developers select one of them when implementing the component. The simplest next-level architecture is monolithic. This is an all-in-one and black box implementation, and is suitable for small group development, prototyping, and very complex functions. Others are arbitrary architecture consists of sub-components and have advantages for large group development. A sub-component can be combined with others that adopt the same architecture. Third parties can define and publish their own architecture and interface for open source development. It is desirable to propose them for standardization if they are sufficiently evaluated.
"},{"location":"design/autoware-interfaces/#features","title":"Features","text":""},{"location":"design/autoware-interfaces/#communication-methods","title":"Communication methods","text":"As shown in the table below, interfaces are classified into four communication methods to define their behavior. Function Call is a request-response communication and is used for processing that requires immediate results. The others are publish-subscribe communication. Notification is used to process data that changes with some event, typically a callback. Streams handle continuously changing data. Reliable Stream expects all data to arrive without loss, Realtime Stream expects the latest data to arrive with low delay.
Communication Method ROS Implementation Optional Implementation Function Call Service HTTP Notification Topic (reliable, transient_local) MQTT (QoS=2, retain) Reliable Stream Topic (reliable, volatile) MQTT (QoS=2) Realtime Stream Topic (best_effort, volatile) MQTT (QoS=0)These methods are provided as services or topics of ROS since Autoware is developed using ROS and mainly communicates with its packages. On the other hand, FMS and HMI are often implemented without ROS, Autoware is also expected to communicate with applications that do not use ROS. It is wasteful for each of these applications to have an adapter for Autoware, and a more suitable means of communication is required. HTTP and MQTT are suggested as additional options because these protocols are widely used and can substitute the behavior of services and topics. In that case, text formats such as JSON where field names are repeated in an array of objects, are inefficient and it is necessary to consider the serialization.
"},{"location":"design/autoware-interfaces/#naming-convention","title":"Naming convention","text":"The name of the interface must be /<component name>/api/<interface name>, where <component name> is the name of the component. For an AD API component, omit this part and start with /api. The <interface name> is an arbitrary string separated by slashes. Note that this rule causes a restriction that the namespace api must not be used as a name other than AD API and the component interface.
The following are examples of correct interface names for AD API and the component interface:
The following are examples of incorrect interface names for AD API and the component interface:
It is recommended to log the interface for analysis of vehicle behavior. If logging is needed, rosbag is available for topics, and use logger in rclcpp or rclpy for services. Typically, create a wrapper for service and client classes that logs when a service is called.
"},{"location":"design/autoware-interfaces/#restrictions","title":"Restrictions","text":"For each API, consider the restrictions such as following and describe them if necessary.
Services:
Topics:
Do not share the types in AD API unless they are obviously the same to avoid changes in one API affecting another. Also, implementation-dependent types, including the component interface, should not be used in AD API for the same reason. Use the type in AD API in implementation, or create the same type and copy the data to convert the type.
"},{"location":"design/autoware-interfaces/#constants-and-enumeration","title":"Constants and enumeration","text":"Since ROS don't support enumeration, use constants instead. The default value of type such as zero and empty string should not be used to detect that a variable is unassigned. Alternatively, assign it a dedicated name to indicate that it is undefined. If one type has multiple enumerations, comment on the correspondence between constants and variables. Do not use enumeration values directly, as assignments are subject to change when the version is updated.
"},{"location":"design/autoware-interfaces/#time-stamp","title":"Time stamp","text":"Clarify what the timestamp indicates. for example, send time, measurement time, update time, etc. Consider having multiple timestamps if necessary. Use std_msgs/msg/Header when using ROS transform. Also consider whether the header is common to all data, independent for each data, or additional timestamp is required.
Currently, there is no required header.
"},{"location":"design/autoware-interfaces/#response-status","title":"Response status","text":"The interfaces whose communication method is Function Call use a common response status to unify the error format. These interfaces should include a variable of ResponseStatus with the name status in the response. See autoware_adapi_v1_msgs/msg/ResponseStatus for details.
"},{"location":"design/autoware-interfaces/#concerns-assumptions-and-limitations","title":"Concerns, assumptions and limitations","text":"Warning
Under Construction
See here for an overview.
"},{"location":"design/autoware-interfaces/ad-api/list/","title":"List of Autoware AD API","text":""},{"location":"design/autoware-interfaces/ad-api/list/#list-of-autoware-ad-api","title":"List of Autoware AD API","text":"This API manages the behavior related to the abnormality of the vehicle. It provides the state of Request to Intervene (RTI), Minimal Risk Maneuver (MRM) and Minimal Risk Condition (MRC). As shown below, Autoware has the gate to switch between the command during normal operation and the command during abnormal operation. For safety, Autoware switches the operation to MRM when an abnormality is detected. Since the required behavior differs depending on the situation, MRM is implemented in various places as a specific mode in a normal module or as an independent module. The fail-safe module selects the behavior of MRM according to the abnormality and switches the gate output to that command.
"},{"location":"design/autoware-interfaces/ad-api/list/api/fail_safe/#states","title":"States","text":"The MRM state indicates whether MRM is operating. This state also provides success or failure. Generally, MRM will switch to another behavior if it fails.
State Description NONE MRM is not operating. OPERATING MRM is operating because an abnormality has been detected. SUCCEEDED MRM succeeded. The vehicle is in a safe condition. FAILED MRM failed. The vehicle is still in an unsafe condition."},{"location":"design/autoware-interfaces/ad-api/list/api/fail_safe/#behavior","title":"Behavior","text":"There is a dependency between MRM behaviors. For example, it switches from a comfortable stop to a emergency stop, but not the other way around. This is service dependent. Autoware supports the following transitions by default.
State Description NONE MRM is not operating or is operating but no special behavior is required. COMFORTABLE_STOP The vehicle will stop quickly with a comfortable deceleration. EMERGENCY_STOP The vehicle will stop immediately with as much deceleration as possible."},{"location":"design/autoware-interfaces/ad-api/list/api/fail_safe/mrm_state/","title":"Mrm state","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/fail_safe/mrm_state/#apifail_safemrm_state","title":"/api/fail_safe/mrm_state","text":"Get the MRM state. For details, see the fail-safe.
"},{"location":"design/autoware-interfaces/ad-api/list/api/fail_safe/mrm_state/#message","title":"Message","text":"Name Type Description state uint16 The state of MRM operation. behavior uint16 The currently selected behavior of MRM."},{"location":"design/autoware-interfaces/ad-api/list/api/interface/","title":"Interface API","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/interface/#interface-api","title":"Interface API","text":"This API provides the interface version of the set of AD APIs. It follows Semantic Versioning in order to provide an intuitive understanding of the changes between versions.
"},{"location":"design/autoware-interfaces/ad-api/list/api/interface/version/","title":"Version","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/interface/version/#apiinterfaceversion","title":"/api/interface/version","text":"Get the interface version. The version follows Semantic Versioning.
"},{"location":"design/autoware-interfaces/ad-api/list/api/interface/version/#request","title":"Request","text":"None
"},{"location":"design/autoware-interfaces/ad-api/list/api/interface/version/#response","title":"Response","text":"Name Type Description major uint16 major version minor uint16 minor version patch uint16 patch version"},{"location":"design/autoware-interfaces/ad-api/list/api/localization/","title":"Localization API","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/localization/#localization-api","title":"Localization API","text":"This API manages the initialization of localization. Autoware requires a global pose as the initial guess for localization.
"},{"location":"design/autoware-interfaces/ad-api/list/api/localization/#states","title":"States","text":"State Description UNINITIALIZED Localization is not initialized. Waiting for a global pose as the initial guess. INITIALIZING Localization is initializing. INITIALIZED Localization is initialized. Initialization can be requested again if necessary."},{"location":"design/autoware-interfaces/ad-api/list/api/localization/initialization_state/","title":"Initialization state","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/localization/initialization_state/#apilocalizationinitialization_state","title":"/api/localization/initialization_state","text":"Get the initialization state of localization. For details, see the localization initialization state.
"},{"location":"design/autoware-interfaces/ad-api/list/api/localization/initialization_state/#message","title":"Message","text":"Name Type Description state uint16 A value of the localization initialization state."},{"location":"design/autoware-interfaces/ad-api/list/api/localization/initialize/","title":"Initialize","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/localization/initialize/#apilocalizationinitialize","title":"/api/localization/initialize","text":"Request to initialize localization. For details, see the pose state.
"},{"location":"design/autoware-interfaces/ad-api/list/api/localization/initialize/#request","title":"Request","text":"Name Type Description pose geometry_msgs/msg/PoseWithCovarianceStamped[<=1] A global pose as the initial guess. If omitted, the GNSS pose will be used."},{"location":"design/autoware-interfaces/ad-api/list/api/localization/initialize/#response","title":"Response","text":"Name Type Description status autoware_adapi_v1_msgs/msg/ResponseStatus response status"},{"location":"design/autoware-interfaces/ad-api/list/api/motion/","title":"Motion API","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/motion/#motion-api","title":"Motion API","text":"This API manages the current behavior of the vehicle. Applications can notify the vehicle behavior to the people around and visualize it for operator and passengers.
"},{"location":"design/autoware-interfaces/ad-api/list/api/motion/#states","title":"States","text":"The motion state manages the stop and start of the vehicle. Once the vehicle has stopped, the state will be STOPPED. After this, when the vehicle tries to start (is still stopped), the state will be STARTING. In this state, calling the start API changes the state to MOVING and the vehicle starts. This mechanism can add processing such as announcements before the vehicle starts. Depending on the configuration, the state may transition directly from STOPPED to MOVING.
State Description STOPPED The vehicle is stopped. STARTING The vehicle is stopped, but is trying to start. MOVING The vehicle is moving. BRAKING (T.B.D.) The vehicle is decelerating strongly."},{"location":"design/autoware-interfaces/ad-api/list/api/motion/accept_start/","title":"Accept start","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/motion/accept_start/#apimotionaccept_start","title":"/api/motion/accept_start","text":"Accept the vehicle to start. This API can be used when the motion state is STARTING.
"},{"location":"design/autoware-interfaces/ad-api/list/api/motion/accept_start/#request","title":"Request","text":"None
"},{"location":"design/autoware-interfaces/ad-api/list/api/motion/accept_start/#response","title":"Response","text":"Name Type Description status autoware_adapi_v1_msgs/msg/ResponseStatus response status"},{"location":"design/autoware-interfaces/ad-api/list/api/motion/state/","title":"State","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/motion/state/#apimotionstate","title":"/api/motion/state","text":"Get the motion state. For details, see the motion state.
"},{"location":"design/autoware-interfaces/ad-api/list/api/motion/state/#message","title":"Message","text":"Name Type Description state uint16 A value of the motion state."},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/","title":"Operation Mode API","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/#operation-mode-api","title":"Operation Mode API","text":"As shown below, Autoware assumes that the vehicle interface has two modes, Autoware control and direct control. In direct control mode, the vehicle is operated using devices such as steering and pedals. If the vehicle does not support direct control mode, it is always treated as Autoware control mode. Autoware control mode has four operation modes.
Mode Description Stop Keep the vehicle stopped. Autonomous Autonomously control the vehicle. Local Manually control the vehicle from nearby with some device such as a joystick. Remote Manually control the vehicle from a web application on the cloud. "},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/#states","title":"States","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/#autoware-control-flag","title":"Autoware control flag","text":"The flag is_autoware_control_enabled indicates if the vehicle is controlled by Autoware. The enable and disable APIs can be used if the control can be switched by software. These APIs will always fail if the vehicle does not support mode switching or is switched by hardware.
The state operation_mode indicates what command is used when Autoware control is enabled. The flags change_to_* can be used to check if it is possible to transition to each mode.
Since Autoware may not be able to guarantee safety, such as switching to autonomous mode during overspeed. There is the flag is_in_transition for this situation and it will be true when changing modes. The operator who changed the mode should ensure safety while this flag is true. The flag will be false when the mode change is complete.
Change the operation mode to autonomous. For details, see the operation mode.
"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/change_to_autonomous/#request","title":"Request","text":"None
"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/change_to_autonomous/#response","title":"Response","text":"Name Type Description status autoware_adapi_v1_msgs/msg/ResponseStatus response status"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/change_to_local/","title":"Change to local","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/change_to_local/#apioperation_modechange_to_local","title":"/api/operation_mode/change_to_local","text":"Change the operation mode to local. For details, see the operation mode.
"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/change_to_local/#request","title":"Request","text":"None
"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/change_to_local/#response","title":"Response","text":"Name Type Description status autoware_adapi_v1_msgs/msg/ResponseStatus response status"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/change_to_remote/","title":"Change to remote","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/change_to_remote/#apioperation_modechange_to_remote","title":"/api/operation_mode/change_to_remote","text":"Change the operation mode to remote. For details, see the operation mode.
"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/change_to_remote/#request","title":"Request","text":"None
"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/change_to_remote/#response","title":"Response","text":"Name Type Description status autoware_adapi_v1_msgs/msg/ResponseStatus response status"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/change_to_stop/","title":"Change to stop","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/change_to_stop/#apioperation_modechange_to_stop","title":"/api/operation_mode/change_to_stop","text":"Change the operation mode to stop. For details, see the operation mode.
"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/change_to_stop/#request","title":"Request","text":"None
"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/change_to_stop/#response","title":"Response","text":"Name Type Description status autoware_adapi_v1_msgs/msg/ResponseStatus response status"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/disable_autoware_control/","title":"Disable autoware control","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/disable_autoware_control/#apioperation_modedisable_autoware_control","title":"/api/operation_mode/disable_autoware_control","text":"Disable vehicle control by Autoware. For details, see the operation mode. This API fails if the vehicle does not support mode change by software.
"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/disable_autoware_control/#request","title":"Request","text":"None
"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/disable_autoware_control/#response","title":"Response","text":"Name Type Description status autoware_adapi_v1_msgs/msg/ResponseStatus response status"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/enable_autoware_control/","title":"Enable autoware control","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/enable_autoware_control/#apioperation_modeenable_autoware_control","title":"/api/operation_mode/enable_autoware_control","text":"Enable vehicle control by Autoware. For details, see the operation mode. This API fails if the vehicle does not support mode change by software.
"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/enable_autoware_control/#request","title":"Request","text":"None
"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/enable_autoware_control/#response","title":"Response","text":"Name Type Description status autoware_adapi_v1_msgs/msg/ResponseStatus response status"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/state/","title":"State","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/state/#apioperation_modestate","title":"/api/operation_mode/state","text":"Get the operation mode state. For details, see the operation mode.
"},{"location":"design/autoware-interfaces/ad-api/list/api/operation_mode/state/#message","title":"Message","text":"Name Type Description mode uint8 The selected command for Autoware control. is_autoware_control_enabled bool True if vehicle control by Autoware is enabled. is_in_transition bool True if the operation mode is in transition. is_stop_mode_available bool True if the operation mode can be changed to stop. is_autonomous_mode_available bool True if the operation mode can be changed to autonomous. is_local_mode_available bool True if the operation mode can be changed to local. is_remote_mode_available bool True if the operation mode can be changed to remote."},{"location":"design/autoware-interfaces/ad-api/list/api/planning/","title":"Planning API","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/planning/#planning-api","title":"Planning API","text":"This API manages the planned behavior of the vehicle. Applications can notify the vehicle behavior to the people around and visualize it for operator and passengers.
"},{"location":"design/autoware-interfaces/ad-api/list/api/planning/#velocity-factors","title":"Velocity factors","text":"The velocity factors is an array of information on the behavior that the vehicle stops (or slows down). Each factor has a type shown below, pose in the base link, distance, status, and detailed data depending on its type. As the vehicle approaches the stop position, this factor appears with a status of APPROACHING. And when the vehicle reaches that position and stops, the status will be STOPPED. The pose indicates the stop position or the base link if the stop position cannot be calculated.
Factor Type Description SURROUNDING_OBSTACLE There are obstacles immediately around the vehicle. ROUTE_OBSTACLE There are obstacles along the route ahead. INTERSECTION There are obstacles in other lanes in the path. CROSSWALK There are obstacles on the crosswalk. REAR_CHECK There are obstacles behind that would be in a human driver's blind spot. USER_DEFINED_DETECTION_AREA There are obstacles in the predefined detection area. NO_STOPPING_AREA There is not enough space beyond the no stopping area. STOP_SIGN A stop by a stop sign. TRAFFIC_SIGNAL A stop by a traffic signal. V2I_GATE_CONTROL_ENTER A stop by a V2I gate entering. V2I_GATE_CONTROL_LEAVE A stop by a V2I gate leaving. MERGE A stop before merging lanes. SIDEWALK A stop before crossing the sidewalk. LANE_CHANGE A lane change. AVOIDANCE A path change to avoid an obstacle in the current lane. EMERGENCY_OPERATION A stop by emergency instruction from the operator."},{"location":"design/autoware-interfaces/ad-api/list/api/planning/#steering-factors","title":"Steering factors","text":"The steering factors is an array of information on the maneuver that requires use of turn indicators, such as turning left or right. Each factor has a type shown below, pose in the base link, distance, status, and detailed data depending on its type. As the vehicle approaches the position to start steering, this factor appears with a status of APPROACHING. And when the vehicle reaches that position, the status will be TURNING. The pose indicates the start position when APPROACHING and the end position when TURNING.
In cases such as lane change and avoidance, the vehicle will start steering at any position in the range depending on the situation. As the vehicle approaches the start position of the range, this factor appears with a status of APPROACHING. And when the vehicle reaches that position, the status will be TRYING. Then, when it is possible, the vehicle will start steering and the status will be TURNING. The pose indicates the start of the range (A) when APPROACHING and the end of the range (B) when TRYING. The position to end steering (C to D) for TURNING depends on the position to start steering.
Factor Type Description INTERSECTION A turning left or right at an intersection. LANE_CHANGE A lane change. AVOIDANCE_PATH_CHANGE A path change to avoid an obstacle in the current lane. AVOIDANCE_PATH_RETURN A path change to return to the original lane after avoiding an obstacle. STATION T.B.D. (bus stop) PULL_OUT T.B.D. PULL_OVER T.B.D. EMERGENCY_OPERATION A path change by emergency instruction from the operator."},{"location":"design/autoware-interfaces/ad-api/list/api/planning/steering_factors/","title":"Steering factors","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/planning/steering_factors/#apiplanningsteering_factors","title":"/api/planning/steering_factors","text":"Get the steering factors, sorted in ascending order of distance. For details, see the planning.
"},{"location":"design/autoware-interfaces/ad-api/list/api/planning/steering_factors/#message","title":"Message","text":"Name Type Description factors.pose geometry_msgs/msg/Pose[2] The base link pose related to the steering factor. factors.distance float32[2] The distance from the base link to the above pose. factors.type uint16 The type of the steering factor. factors.direction uint16 The direction of the steering factor. factors.status uint16 The status of the steering factor. factors.detail string The additional information of the steering factor."},{"location":"design/autoware-interfaces/ad-api/list/api/planning/velocity_factors/","title":"Velocity factors","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/planning/velocity_factors/#apiplanningvelocity_factors","title":"/api/planning/velocity_factors","text":"Get the velocity factors, sorted in ascending order of distance. For details, see the planning.
"},{"location":"design/autoware-interfaces/ad-api/list/api/planning/velocity_factors/#message","title":"Message","text":"Name Type Description factors.pose geometry_msgs/msg/Pose The base link pose related to the velocity factor. factors.distance float32 The distance from the base link to the above pose. factors.type uint16 The type of the velocity factor. factors.status uint16 The status of the velocity factor. factors.detail string The additional information of the velocity factor."},{"location":"design/autoware-interfaces/ad-api/list/api/routing/","title":"Route API","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/routing/#route-api","title":"Route API","text":"This API manages destination and waypoints. Note that waypoints are not like stops and just points passing through. In other words, Autoware does not support the route with multiple stops, the application needs to split it up and switch them. There are two ways to set the route. The one is a generic method that uses pose, another is a map-dependent.
"},{"location":"design/autoware-interfaces/ad-api/list/api/routing/#states","title":"States","text":"State Description UNSET The route is not set. Waiting for a route request. SET The route is set. ARRIVED The vehicle has arrived at the destination. CHANGING Trying to change the route. Not implemented yet."},{"location":"design/autoware-interfaces/ad-api/list/api/routing/clear_route/","title":"Clear route","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/routing/clear_route/#apiroutingclear_route","title":"/api/routing/clear_route","text":"Clear the route.
"},{"location":"design/autoware-interfaces/ad-api/list/api/routing/clear_route/#request","title":"Request","text":"None
"},{"location":"design/autoware-interfaces/ad-api/list/api/routing/clear_route/#response","title":"Response","text":"Name Type Description status autoware_adapi_v1_msgs/msg/ResponseStatus response status"},{"location":"design/autoware-interfaces/ad-api/list/api/routing/route/","title":"Route","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/routing/route/#apiroutingroute","title":"/api/routing/route","text":"Get the route with the waypoint segments in lanelet format. It is empty if route is not set.
"},{"location":"design/autoware-interfaces/ad-api/list/api/routing/route/#message","title":"Message","text":"Name Type Description header std_msgs/msg/Header header for pose transformation data autoware_adapi_v1_msgs/msg/RouteData[<=1] The route in lanelet format"},{"location":"design/autoware-interfaces/ad-api/list/api/routing/set_route/","title":"Set route","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/routing/set_route/#apiroutingset_route","title":"/api/routing/set_route","text":"Set the route with the waypoint segments in lanelet format. If start pose is not specified, the current pose will be used.
"},{"location":"design/autoware-interfaces/ad-api/list/api/routing/set_route/#request","title":"Request","text":"Name Type Description header std_msgs/msg/Header header for pose transformation goal geometry_msgs/msg/Pose goal pose segments autoware_adapi_v1_msgs/msg/RouteSegment[] waypoint segments in lanelet format"},{"location":"design/autoware-interfaces/ad-api/list/api/routing/set_route/#response","title":"Response","text":"Name Type Description status autoware_adapi_v1_msgs/msg/ResponseStatus response status"},{"location":"design/autoware-interfaces/ad-api/list/api/routing/set_route_points/","title":"Set route points","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/routing/set_route_points/#apiroutingset_route_points","title":"/api/routing/set_route_points","text":"Set the route with the waypoint poses. If start pose is not specified, the current pose will be used.
"},{"location":"design/autoware-interfaces/ad-api/list/api/routing/set_route_points/#request","title":"Request","text":"Name Type Description header std_msgs/msg/Header header for pose transformation goal geometry_msgs/msg/Pose goal pose waypoints geometry_msgs/msg/Pose[] waypoint poses"},{"location":"design/autoware-interfaces/ad-api/list/api/routing/set_route_points/#response","title":"Response","text":"Name Type Description status autoware_adapi_v1_msgs/msg/ResponseStatus response status"},{"location":"design/autoware-interfaces/ad-api/list/api/routing/state/","title":"State","text":""},{"location":"design/autoware-interfaces/ad-api/list/api/routing/state/#apiroutingstate","title":"/api/routing/state","text":"Get the route state. For details, see the route state.
"},{"location":"design/autoware-interfaces/ad-api/list/api/routing/state/#message","title":"Message","text":"Name Type Description state uint16 A value of the route state."},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/localization_initialization_state/","title":"Localization initialization state","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/localization_initialization_state/#autoware_adapi_v1_msgsmsglocalizationinitializationstate","title":"autoware_adapi_v1_msgs/msg/LocalizationInitializationState","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/localization_initialization_state/#definition","title":"Definition","text":"uint16 UNKNOWN = 0\nuint16 UNINITIALIZED = 1\nuint16 INITIALIZING = 2\nuint16 INITIALIZED = 3\n\nbuiltin_interfaces/Time stamp\nuint16 state\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/localization_initialization_state/#this-type-is-used-by","title":"This type is used by","text":"None
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/motion_state/","title":"Motion state","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/motion_state/#autoware_adapi_v1_msgsmsgmotionstate","title":"autoware_adapi_v1_msgs/msg/MotionState","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/motion_state/#definition","title":"Definition","text":"uint16 UNKNOWN = 0\nuint16 STOPPED = 1\nuint16 STARTING = 2\nuint16 MOVING = 3\n\nbuiltin_interfaces/Time stamp\nuint16 state\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/motion_state/#this-type-is-used-by","title":"This type is used by","text":"None
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/mrm_state/","title":"Mrm state","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/mrm_state/#autoware_adapi_v1_msgsmsgmrmstate","title":"autoware_adapi_v1_msgs/msg/MrmState","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/mrm_state/#definition","title":"Definition","text":"builtin_interfaces/Time stamp\n\n# For common use\nuint16 UNKNOWN = 0\n\n# For state\nuint16 NORMAL = 1\nuint16 MRM_OPERATING = 2\nuint16 MRM_SUCCEEDED = 3\nuint16 MRM_FAILED = 4\n\n# For behavior\nuint16 NONE = 1\nuint16 EMERGENCY_STOP = 2\nuint16 COMFORTABLE_STOP = 3\n\nuint16 state\nuint16 behavior\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/mrm_state/#this-type-is-used-by","title":"This type is used by","text":"None
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/operation_mode_state/","title":"Operation mode state","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/operation_mode_state/#autoware_adapi_v1_msgsmsgoperationmodestate","title":"autoware_adapi_v1_msgs/msg/OperationModeState","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/operation_mode_state/#definition","title":"Definition","text":"# constants for mode\nuint8 UNKNOWN = 0\nuint8 STOP = 1\nuint8 AUTONOMOUS = 2\nuint8 LOCAL = 3\nuint8 REMOTE = 4\n\n# variables\nbuiltin_interfaces/Time stamp\nuint8 mode\nbool is_autoware_control_enabled\nbool is_in_transition\nbool is_stop_mode_available\nbool is_autonomous_mode_available\nbool is_local_mode_available\nbool is_remote_mode_available\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/operation_mode_state/#this-type-is-used-by","title":"This type is used by","text":"None
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/response_status/","title":"Response status","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/response_status/#autoware_adapi_v1_msgsmsgresponsestatus","title":"autoware_adapi_v1_msgs/msg/ResponseStatus","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/response_status/#definition","title":"Definition","text":"# error code\nuint16 UNKNOWN = 50000\nuint16 SERVICE_UNREADY = 50001\nuint16 SERVICE_TIMEOUT = 50002\nuint16 TRANSFORM_ERROR = 50003\nuint16 PARAMETER_ERROR = 50004\n\n# warning code\nuint16 DEPRECATED = 60000\nuint16 NO_EFFECT = 60001\n\n# variables\nbool success\nuint16 code\nstring message\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/response_status/#this-type-is-used-by","title":"This type is used by","text":"std_msgs/Header header\nautoware_adapi_v1_msgs/RouteData[<=1] data\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/route_data/","title":"Route data","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/route_data/#autoware_adapi_v1_msgsmsgroutedata","title":"autoware_adapi_v1_msgs/msg/RouteData","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/route_data/#definition","title":"Definition","text":"geometry_msgs/Pose start\ngeometry_msgs/Pose goal\nautoware_adapi_v1_msgs/RouteSegment[] segments\nint64 id\nstring type # The same id may be used for each type.\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/route_primitive/#this-type-is-used-by","title":"This type is used by","text":"autoware_adapi_v1_msgs/RoutePrimitive preferred\nautoware_adapi_v1_msgs/RoutePrimitive[] alternatives # Does not include the preferred primitive.\nuint16 UNKNOWN = 0\nuint16 UNSET = 1\nuint16 SET = 2\nuint16 ARRIVED = 3\nuint16 CHANGING = 4\n\nbuiltin_interfaces/Time stamp\nuint16 state\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/route_state/#this-type-is-used-by","title":"This type is used by","text":"None
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/steering_factor/","title":"Steering factor","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/steering_factor/#autoware_adapi_v1_msgsmsgsteeringfactor","title":"autoware_adapi_v1_msgs/msg/SteeringFactor","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/steering_factor/#definition","title":"Definition","text":"# constants for common use\nuint16 UNKNOWN = 0\n\n# constants for type\nuint16 INTERSECTION = 1\nuint16 LANE_CHANGE = 2\nuint16 AVOIDANCE_PATH_CHANGE = 3\nuint16 AVOIDANCE_PATH_RETURN = 4\nuint16 STATION = 5\nuint16 PULL_OUT = 6\nuint16 PULL_OVER = 7\nuint16 EMERGENCY_OPERATION = 8\n\n# constants for direction\nuint16 LEFT = 1\nuint16 RIGHT = 2\nuint16 STRAIGHT = 3\n\n# constants for status\nuint16 APPROACHING = 1\nuint16 TRYING = 2\nuint16 TURNING = 3\n\n# variables\ngeometry_msgs/Pose[2] pose\nfloat32[2] distance\nuint16 type\nuint16 direction\nuint16 status\nstring detail\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/steering_factor/#this-type-is-used-by","title":"This type is used by","text":"std_msgs/Header header\nautoware_adapi_v1_msgs/SteeringFactor[] factors\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/velocity_factor/","title":"Velocity factor","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/velocity_factor/#autoware_adapi_v1_msgsmsgvelocityfactor","title":"autoware_adapi_v1_msgs/msg/VelocityFactor","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/velocity_factor/#definition","title":"Definition","text":"# constants for common use\nuint16 UNKNOWN = 0\n\n# constants for type\nuint16 SURROUNDING_OBSTACLE = 1\nuint16 ROUTE_OBSTACLE = 2\nuint16 INTERSECTION = 3\nuint16 CROSSWALK = 4\nuint16 REAR_CHECK = 5\nuint16 USER_DEFINED_DETECTION_AREA = 6\nuint16 NO_STOPPING_AREA = 7\nuint16 STOP_SIGN = 8\nuint16 TRAFFIC_SIGNAL = 9\nuint16 V2I_GATE_CONTROL_ENTER = 10\nuint16 V2I_GATE_CONTROL_LEAVE = 11\nuint16 MERGE = 12\nuint16 SIDEWALK = 13\nuint16 LANE_CHANGE = 14\nuint16 AVOIDANCE = 15\nuint16 EMERGENCY_STOP_OPERATION = 16\n\n# constants for status\nuint16 APPROACHING = 1\nuint16 STOPPED = 2\n\n# variables\ngeometry_msgs/Pose pose\nfloat32 distance\nuint16 type\nuint16 status\nstring detail\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/msg/velocity_factor/#this-type-is-used-by","title":"This type is used by","text":"std_msgs/Header header\nautoware_adapi_v1_msgs/VelocityFactor[] factors\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/accept_start/","title":"Accept start","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/accept_start/#autoware_adapi_v1_msgssrvacceptstart","title":"autoware_adapi_v1_msgs/srv/AcceptStart","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/accept_start/#definition","title":"Definition","text":"---\nuint16 ERROR_NOT_STARTING = 1\nautoware_adapi_v1_msgs/ResponseStatus status\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/change_operation_mode/","title":"Change operation mode","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/change_operation_mode/#autoware_adapi_v1_msgssrvchangeoperationmode","title":"autoware_adapi_v1_msgs/srv/ChangeOperationMode","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/change_operation_mode/#definition","title":"Definition","text":"---\nuint16 ERROR_NOT_AVAILABLE = 1\nuint16 ERROR_IN_TRANSITION = 2\nautoware_adapi_v1_msgs/ResponseStatus status\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/clear_route/","title":"Clear route","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/clear_route/#autoware_adapi_v1_msgssrvclearroute","title":"autoware_adapi_v1_msgs/srv/ClearRoute","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/clear_route/#definition","title":"Definition","text":"---\nautoware_adapi_v1_msgs/ResponseStatus status\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/initialize_localization/","title":"Initialize localization","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/initialize_localization/#autoware_adapi_v1_msgssrvinitializelocalization","title":"autoware_adapi_v1_msgs/srv/InitializeLocalization","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/initialize_localization/#definition","title":"Definition","text":"geometry_msgs/PoseWithCovarianceStamped[<=1] pose\n---\nuint16 ERROR_UNSAFE = 1\nuint16 ERROR_GNSS_SUPPORT = 2\nuint16 ERROR_GNSS = 3\nuint16 ERROR_ESTIMATION = 4\nautoware_adapi_v1_msgs/ResponseStatus status\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/set_route/","title":"Set route","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/set_route/#autoware_adapi_v1_msgssrvsetroute","title":"autoware_adapi_v1_msgs/srv/SetRoute","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/set_route/#definition","title":"Definition","text":"std_msgs/Header header\ngeometry_msgs/Pose goal\nautoware_adapi_v1_msgs/RouteSegment[] segments\n---\nuint16 ERROR_ROUTE_EXISTS = 1\nuint16 ERROR_PLANNER_UNREADY = 2\nuint16 ERROR_PLANNER_FAILED = 3\nautoware_adapi_v1_msgs/ResponseStatus status\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/set_route_points/","title":"Set route points","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/set_route_points/#autoware_adapi_v1_msgssrvsetroutepoints","title":"autoware_adapi_v1_msgs/srv/SetRoutePoints","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_v1_msgs/srv/set_route_points/#definition","title":"Definition","text":"std_msgs/Header header\ngeometry_msgs/Pose goal\ngeometry_msgs/Pose[] waypoints\n---\nuint16 ERROR_ROUTE_EXISTS = 1\nuint16 ERROR_PLANNER_UNREADY = 2\nuint16 ERROR_PLANNER_FAILED = 3\nautoware_adapi_v1_msgs/ResponseStatus status\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_version_msgs/srv/interface_version/","title":"Interface version","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_version_msgs/srv/interface_version/#autoware_adapi_version_msgssrvinterfaceversion","title":"autoware_adapi_version_msgs/srv/InterfaceVersion","text":""},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_version_msgs/srv/interface_version/#definition","title":"Definition","text":"---\nuint16 major\nuint16 minor\nuint16 patch\nNone
"},{"location":"design/autoware-interfaces/ad-api/types/autoware_adapi_version_msgs/srv/interface_version/#this-type-is-used-by","title":"This type is used by","text":"None
"},{"location":"design/autoware-interfaces/ad-api/use-cases/","title":"Use cases of Autoware AD API","text":""},{"location":"design/autoware-interfaces/ad-api/use-cases/#use-cases-of-autoware-ad-api","title":"Use cases of Autoware AD API","text":""},{"location":"design/autoware-interfaces/ad-api/use-cases/#user-stories","title":"User stories","text":"The user stories are service scenarios that AD API assumes. AD API is designed based on these scenarios. Each scenario is realized by a combination of use cases described later. If there are scenarios that cannot be covered, please discuss adding a user story.
Use cases are partial scenarios derived from the user story and generically designed. Service providers can combine these use cases to define user stories and check if AD API can be applied to their own scenarios.
This user story is a bus service that goes around the designated stops.
"},{"location":"design/autoware-interfaces/ad-api/use-cases/bus-service/#scenario","title":"Scenario","text":"Step Operation Use Case 1 Startup the autonomous driving system. Launch and terminate 2 Drive the vehicle from the garage to the waiting position. Change the operation mode 3 Enable autonomous control. Change the operation mode 4 Drive the vehicle to the next bus stop. Drive to the designated position 5 Get on and off the vehicle. Get on and get off 6 Return to step 4 unless it's the last bus stop. 7 Drive the vehicle to the waiting position. Drive to the designated position 8 Drive the vehicle from the waiting position to the garage. Change the operation mode 9 Shutdown the autonomous driving system. Launch and terminate"},{"location":"design/autoware-interfaces/ad-api/use-cases/change-operation-mode/","title":"Change the operation mode","text":""},{"location":"design/autoware-interfaces/ad-api/use-cases/change-operation-mode/#change-the-operation-mode","title":"Change the operation mode","text":""},{"location":"design/autoware-interfaces/ad-api/use-cases/change-operation-mode/#related-api","title":"Related API","text":"Change the mode with software switch.
Change the mode with hardware switch.
Initialization of the pose using input.
Initialization of the pose using GNSS.
This user story is a taxi service that picks up passengers and drives them to their destination.
"},{"location":"design/autoware-interfaces/ad-api/use-cases/taxi-service/#scenario","title":"Scenario","text":"Step Operation Use Case 1 Startup the autonomous driving system. Launch and terminate 2 Drive the vehicle from the garage to the waiting position. Change the operation mode 3 Enable autonomous control. Change the operation mode 4 Drive the vehicle to the position to pick up. Drive to the designated position 5 Get on the vehicle. Get on and get off 6 Drive the vehicle to the destination. Drive to the designated position 7 Get off the vehicle. Get on and get off 8 Drive the vehicle to the waiting position. Drive to the designated position 9 Return to step 4 if there is another request. 10 Drive the vehicle from the waiting position to the garage. Change the operation mode 11 Shutdown the autonomous driving system. Launch and terminate"},{"location":"design/autoware-interfaces/components/","title":"Component interfaces","text":""},{"location":"design/autoware-interfaces/components/#component-interfaces","title":"Component interfaces","text":"Warning
Under Construction
See here for an overview.
"},{"location":"design/autoware-interfaces/components/control/","title":"Control","text":""},{"location":"design/autoware-interfaces/components/control/#control","title":"Control","text":""},{"location":"design/autoware-interfaces/components/control/#inputs","title":"Inputs","text":""},{"location":"design/autoware-interfaces/components/control/#vehicle-kinematic-state","title":"Vehicle kinematic state","text":"Current position and orientation of ego. Published by the Localization module.
trajectory to be followed by the controller. See Outputs of Planning.
"},{"location":"design/autoware-interfaces/components/control/#steering-status","title":"Steering Status","text":"Current steering of the ego vehicle. Published by the Vehicle Interface.
Actuation status of the ego vehicle for acceleration, steering, and brake.
TODO This represents the reported physical efforts exerted by the vehicle actuators. Published by the Vehicle Interface.
A motion signal to drive the vehicle, achieved by the low-level controller in the vehicle layer. Used by the Vehicle Interface.
Environment map created with point cloud, published by the map server.
A 3d point cloud map is used for LiDAR-based localization in Autoware.
"},{"location":"design/autoware-interfaces/components/localization/#manual-initial-pose","title":"Manual Initial Pose","text":"Start pose of ego, published by the user interface.
LiDAR scanning for NDT matching, published by the LiDAR sensor.
The raw 3D-LiDAR data needs to be processed by the point cloud pre-processing modules before being used for localization.
"},{"location":"design/autoware-interfaces/components/localization/#automatic-initial-pose","title":"Automatic Initial pose","text":"Start pose of ego, calculated from INS(Inertial navigation sensor) sensing data.
When the initial pose is not set manually, the message can be used for automatic pose initialization.
Current Geographic coordinate of the ego, published by the GNSS sensor.
Current orientation of the ego, published by the GNSS-INS.
Current orientation, angular velocity and linear acceleration of ego, calculated from IMU sensing data.
Current velocity of the ego vehicle, published by the vehicle interface.
Before the velocity input localization interface, module vehicle_velocity_converter converts message type autoware_auto_vehicle_msgs/msg/VelocityReport to geometry_msgs/msg/TwistWithCovarianceStamped.
Current pose of ego, calculated from localization interface.
Current velocity of ego, calculated from localization interface.
Current acceleration of ego, calculated from localization interface.
Current pose, velocity and acceleration of ego, calculated from localization interface.
Note: Kinematic state contains pose, velocity and acceleration. In the future, pose, velocity and acceleration will not be used as output for localization.
The message will be subscribed by the planning and control module.
"},{"location":"design/autoware-interfaces/components/localization/#localization-accuracy","title":"Localization Accuracy","text":"Diagnostics information that indicates if the localization module works properly.
TBD.
"},{"location":"design/autoware-interfaces/components/map/","title":"Map","text":""},{"location":"design/autoware-interfaces/components/map/#map","title":"Map","text":""},{"location":"design/autoware-interfaces/components/map/#overview","title":"Overview","text":"Autoware relies on high-definition point cloud maps and vector maps of the driving environment to perform various tasks. Before launching Autoware, you need to load the pre-created map files.
"},{"location":"design/autoware-interfaces/components/map/#inputs","title":"Inputs","text":".pcd).osm)Refer to Creating maps on how to create maps.
"},{"location":"design/autoware-interfaces/components/map/#outputs","title":"Outputs","text":""},{"location":"design/autoware-interfaces/components/map/#point-cloud-map","title":"Point cloud map","text":"It loads point cloud files and publishes the maps to the other Autoware nodes in various configurations. Currently, it supports the following types:
It loads a Lanelet2 file and publishes the map data as autoware_auto_mapping_msgs/msg/HADMapBin message. The lan/lon coordinates are projected onto the MGRS coordinates.
Visualize autoware_auto_mapping_msgs/HADMapBin messages in Rviz.
Warning
Under Construction
This page provides specific specifications about the Interface of the Perception Component. Please refer to the perception architecture reference implementation design document for concepts and data flow.
"},{"location":"design/autoware-interfaces/components/perception/#input","title":"Input","text":""},{"location":"design/autoware-interfaces/components/perception/#from-map-component","title":"From Map Component","text":"Name Topic / Service Type Description Vector Map/map/vector_map autoware_auto_mapping_msgs/msg/HADMapBin HD Map including the information about lanes Point Cloud Map /service/get_differential_pcd_map autoware_map_msgs/srv/GetDifferentialPointCloudMap Point Cloud Map Notes:
/sensing/camera/camera*/image_rect_color sensor_msgs/Image Camera image data, processed with Lens Distortion Correction (LDC) Camera Image /sensing/camera/camera*/image_raw sensor_msgs/Image Camera image data, not processed with Lens Distortion Correction (LDC) Point Cloud /sensing/lidar/concatenated/pointcloud sensor_msgs/PointCloud2 Concatenated point cloud from multiple LiDAR sources Radar Object /sensing/radar/detected_objects autoware_auto_perception_msgs/msg/DetectedObject Radar objects"},{"location":"design/autoware-interfaces/components/perception/#from-localization-component","title":"From Localization Component","text":"Name Topic Type Description Vehicle Odometry /localization/kinematic_state nav_msgs/msg/Odometry Ego vehicle odometry topic"},{"location":"design/autoware-interfaces/components/perception/#from-api","title":"From API","text":"Name Topic Type Description External Traffic Signals /external/traffic_signals autoware_perception_msgs::msg::TrafficSignalArray The traffic signals from an external system"},{"location":"design/autoware-interfaces/components/perception/#output","title":"Output","text":""},{"location":"design/autoware-interfaces/components/perception/#to-planning","title":"To Planning","text":"Name Topic Type Description Dynamic Objects /perception/object_recognition/objects autoware_auto_perception_msgs/msg/PredictedObjects Set of dynamic objects with information such as a object class and a shape of the objects Obstacles /perception/obstacle_segmentation/pointcloud sensor_msgs/PointCloud2 Obstacles, which includes dynamic objects and static objetcs Occupancy Grid Map /perception/occupancy_grid_map/map nav_msgs/msg/OccupancyGrid The map with the imformation about the presence of obstacles and blind spot Traffic Signal /perception/traffic_light_recognition/traffic_signals autoware_perception_msgs::msg::TrafficSignalArray The traffic signal information such as a color (green, yellow, read) and an arrow (right, left, straight)"},{"location":"design/autoware-interfaces/components/planning/","title":"Planning","text":""},{"location":"design/autoware-interfaces/components/planning/#planning","title":"Planning","text":""},{"location":"design/autoware-interfaces/components/planning/#inputs","title":"Inputs","text":""},{"location":"design/autoware-interfaces/components/planning/#3d-object-predictions","title":"3D Object Predictions","text":"set of perceived objects around ego that need to be avoided when planning a trajectory. Published by the Perception module.
Service response with traffic light information. The message definition is under discussion.
With the traffic_light_state being one of the following
current position and orientation of ego. Published by the Localization module.
map of the environment where the planning takes place. Published by the Map Server.
target pose of ego. Published by the User Interface.
TBD.
The message definition is under discussion.
"},{"location":"design/autoware-interfaces/components/planning/#error-status","title":"Error status","text":"a status corresponding to the current state of Autoware. Used by the Vehicle Interface to switch between different modes in case of emergency. Published by the Diagnostic Manager.
With the state being one of the following:
[TODO] original design for these messages: diagnostic manager also publishes an overriding emergency control command (Add the monitoring system related messages - Autoware.Auto). Possible new design: gate of the vehicle interface switches to the emergency control command (generated by another controller) when receiving an OVERRIDE_REQUESTING message.
The message definition is under discussion.
"},{"location":"design/autoware-interfaces/components/planning/#outputs","title":"Outputs","text":""},{"location":"design/autoware-interfaces/components/planning/#traffic-light-query","title":"Traffic Light Query","text":"service request for the state of a specific traffic light. Sent to the Perception module.
The message definition is under discussion.
"},{"location":"design/autoware-interfaces/components/planning/#trajectory","title":"Trajectory","text":"A sequence of space and velocity points to be followed by the controller.
Commands for various elements of the vehicle unrelated to motion. Sent to the Vehicle Interface. (For the definition, see autoware_auto_vehicle_msgs.)
TBD.
The message definition is under discussion.
"},{"location":"design/autoware-interfaces/components/planning/#engagement-request","title":"Engagement Request","text":"TBD,
The message definition is under discussion.
"},{"location":"design/autoware-interfaces/components/vehicle-dimensions/","title":"Vehicle dimensions","text":""},{"location":"design/autoware-interfaces/components/vehicle-dimensions/#vehicle-dimensions","title":"Vehicle dimensions","text":""},{"location":"design/autoware-interfaces/components/vehicle-dimensions/#vehicle-axes-and-base_link","title":"Vehicle axes and base_link","text":"The base_link frame is used very frequently throughout the Autoware stack, and is a projection of the rear-axle center onto the ground surface.
map to base_link transformation.base_link frame should be in the future.base_link to incoming poses.The distance between front and rear axles.
"},{"location":"design/autoware-interfaces/components/vehicle-dimensions/#track_width","title":"track_width","text":"The distance between left and right wheels.
"},{"location":"design/autoware-interfaces/components/vehicle-dimensions/#overhangs","title":"Overhangs","text":"Overhangs are part of the minimum safety box calculation.
When measuring overhangs, side mirrors, protruding sensors and wheels should be taken into consideration.
"},{"location":"design/autoware-interfaces/components/vehicle-dimensions/#left_overhang","title":"left_overhang","text":"The distance between the axis centers of the left wheels and the left-most point of the vehicle.
"},{"location":"design/autoware-interfaces/components/vehicle-dimensions/#right_overhang","title":"right_overhang","text":"The distance between the axis centers of the right wheels and the right-most point of the vehicle.
"},{"location":"design/autoware-interfaces/components/vehicle-dimensions/#front_overhang","title":"front_overhang","text":"The distance between the front axle and the foremost point of the vehicle.
"},{"location":"design/autoware-interfaces/components/vehicle-dimensions/#rear_overhang","title":"rear_overhang","text":"The distance between the rear axle and the rear-most point of the vehicle.
"},{"location":"design/autoware-interfaces/components/vehicle-dimensions/#vehicle_length","title":"vehicle_length","text":"Total length of the vehicle. Calculated by front_overhang + wheelbase + rear_overhang
Total width of the vehicle. Calculated by left_overhang + track_width + right_overhang
The lateral width of a wheel tire, primarily used for dead reckoning.
"},{"location":"design/autoware-interfaces/components/vehicle-dimensions/#wheel_radius","title":"wheel_radius","text":"The radius of the wheel, primarily used for dead reckoning.
"},{"location":"design/autoware-interfaces/components/vehicle-dimensions/#polygon_footprint","title":"polygon_footprint","text":"The polygon defines the minimum collision area for the vehicle.
The points should be ordered clockwise, with the origin on the base_link.
If the vehicle is going forward, a positive wheel angle will result in the vehicle turning left.
Autoware assumes the rear wheels don't turn on z axis.
The vehicle used in the illustrations was created by xvlblo22 and is from https://www.turbosquid.com/3d-models/modular-sedan-3d-model-1590886.
"},{"location":"design/autoware-interfaces/components/vehicle-interface/","title":"Vehicle Interface","text":""},{"location":"design/autoware-interfaces/components/vehicle-interface/#vehicle-interface","title":"Vehicle Interface","text":"The Vehicle Interface receives the Vehicle Signal Commands and Vehicle Control Commands and publishes the vehicle status. It also communicates with vehicle by the vehicle-specific protocol.
The Gate switches multiple Vehicle Control Commands. These signals include autonomous diving command, joystick, remote control, and emergency operation, etc. The Adapter converts generalized control command (target steering, steering rate, velocity, acceleration, jerk) into vehicle-specific control values (steering-torque, wheel-torque, voltage, pressure, accel pedal position, etc).
(See Inputs of Planning.)
"},{"location":"design/autoware-interfaces/components/vehicle-interface/#vehicle-control-command","title":"Vehicle Control Command","text":"(See Output of Control.)
"},{"location":"design/autoware-interfaces/components/vehicle-interface/#vehicle-signals-commands","title":"Vehicle Signals Commands","text":"Commands for various elements of the vehicle unrelated to motion. Published by the Planning module.
"},{"location":"design/autoware-interfaces/components/vehicle-interface/#outputs","title":"Outputs","text":""},{"location":"design/autoware-interfaces/components/vehicle-interface/#vehicle-signal-reports","title":"Vehicle Signal Reports","text":"Reports for various elements of the vehicle unrelated to motion. Published by the Vehicle Interface.
"},{"location":"design/autoware-interfaces/components/vehicle-interface/#vehicle-odometry","title":"Vehicle Odometry","text":"Odometry of the vehicle. Used by the Localization module to update the pose of the vehicle in the map.
Steering of the ego vehicle. Published by the Vehicle Interface.
Actuation status of the ego vehicle for acceleration, steering, and brake. This represents the reported physical efforts exerted by the vehicle actuators. Published by the Vehicle Interface.
The message definition is under discussion.
"},{"location":"design/autoware-interfaces/components/vehicle-interface/#actuation-command","title":"Actuation Command","text":"Actuation command sent to the ego vehicle. This represents the requested physical efforts to be exerted by the vehicle actuators. Published by the Vehicle Interface as generated by the adapter.
The message definition is under discussion.
"},{"location":"design/autoware-interfaces/components/vehicle-interface/#vehicle-communication","title":"Vehicle Communication","text":"Vehicle specific messages protocol like CAN (Controller Area Network).
"},{"location":"design/configuration-management/","title":"Configuration management","text":""},{"location":"design/configuration-management/#configuration-management","title":"Configuration management","text":"Warning
Under Construction
"},{"location":"design/configuration-management/development-process/","title":"Development process","text":""},{"location":"design/configuration-management/development-process/#development-process","title":"Development process","text":"Warning
Under Construction
"},{"location":"design/configuration-management/release-process/","title":"Release process","text":""},{"location":"design/configuration-management/release-process/#release-process","title":"Release process","text":"Warning
Under Construction
"},{"location":"design/configuration-management/repository-structure/","title":"Repository structure","text":""},{"location":"design/configuration-management/repository-structure/#repository-structure","title":"Repository structure","text":"Warning
Under Construction
"},{"location":"how-to-guides/","title":"How-to guides","text":""},{"location":"how-to-guides/#how-to-guides","title":"How-to guides","text":""},{"location":"how-to-guides/#integrating-autoware","title":"Integrating Autoware","text":"TODO: Write the following contents.
Warning
Under Construction
"},{"location":"how-to-guides/integrating-autoware/launch-autoware/","title":"5. Launch Autoware","text":""},{"location":"how-to-guides/integrating-autoware/launch-autoware/#launch-autoware","title":"Launch Autoware","text":"Warning
Under Construction
"},{"location":"how-to-guides/integrating-autoware/overview/","title":"Overview","text":""},{"location":"how-to-guides/integrating-autoware/overview/#overview","title":"Overview","text":""},{"location":"how-to-guides/integrating-autoware/overview/#requirement-prepare-your-real-vehicle-hardware","title":"Requirement: prepare your real vehicle hardware","text":"Prerequisites for the vehicle:
Create your Autoware meta-repository. One easy way is to fork autowarefoundation/autoware and clone it. For how to fork a repository, refer to GitHub Docs.
git clone https://github.com/YOUR_NAME/autoware.git\nIf you set up multiple types of vehicles, adding a suffix like \"autoware.vehicle_A\" or \"autoware.vehicle_B\" is recommended.
"},{"location":"how-to-guides/integrating-autoware/overview/#2-creating-the-your-vehicle-and-sensor-description","title":"2. Creating the your vehicle and sensor description","text":"Next, you need to create description packages that define the vehicle and sensor configuration of your vehicle.
Create the following two packages:
Once created, you need to update the autoware.repos file of your cloned Autoware repository to refer to these two description packages.
- # sensor_kit\n- sensor_kit/sample_sensor_kit_launch:\n- type: git\n- url: https://github.com/autowarefoundation/sample_sensor_kit_launch.git\n- version: main\n- # vehicle\n- vehicle/sample_vehicle_launch:\n- type: git\n- url: https://github.com/autowarefoundation/sample_vehicle_launch.git\n- version: main\n+ # sensor_kit\n+ sensor_kit/YOUR_SENSOR_KIT_launch:\n+ type: git\n+ url: https://github.com/YOUR_NAME/YOUR_SENSOR_KIT_launch.git\n+ version: main\n+ # vehicle\n+ vehicle/YOUR_VEHICLE_launch:\n+ type: git\n+ url: https://github.com/YOUR_NAME/YOUR_VEHICLE_launch.git\n+ version: main\nDefine URDF and parameters in the vehicle description package (refer to the sample vehicle description package for an example).
"},{"location":"how-to-guides/integrating-autoware/overview/#at-your_vehicle_launch","title":"At YOUR_VEHICLE_launch","text":"Create a launch file (refer to the sample vehicle launch package for example). If you have multiple vehicles with the same hardware setup, you can specify vehicle_id to distinguish them.
Define URDF and extrinsic parameters for all the sensors here (refer to the sample sensor kit description package for example). Note that you need to calibrate extrinsic parameters for all the sensors beforehand.
"},{"location":"how-to-guides/integrating-autoware/overview/#at-your_sensor_kit_launch","title":"At YOUR_SENSOR_KIT_launch","text":"Create launch/sensing.launch.xml that launches the interfaces of all the sensors on the vehicle. (refer to the sample sensor kit launch package for example).
Note
At this point, you are now able to run Autoware's Planning Simulator to do a basic test of your vehicle and sensing packages. To do so, you need to build and install Autoware using your cloned repository. Follow the steps for either Docker or source installation (starting from the dependency installation step) and then run the following command:
ros2 launch autoware_launch planning_simulator.launch.xml vehicle_model:=YOUR_VEHICLE sensor_kit:=YOUR_SENSOR_KIT map_path:=/PATH/TO/YOUR/MAP\nvehicle_interface package","text":"You need to create an interface package for your vehicle. The package is expected to provide the following two functions.
vehicle_cmd_gate and drive the vehicle accordinglyYou can find detailed information about the requirements of the vehicle_interface package in the Vehicle Interface design documentation. You can also refer to TIER IV's pacmod_interface repository as an example of a vehicle interface package.
You need both a pointcloud map and a vector map in order to use Autoware. For more information on map design, please click here.
"},{"location":"how-to-guides/integrating-autoware/overview/#create-a-pointcloud-map","title":"Create a pointcloud map","text":"Use third-party tools such as a LiDAR-based SLAM (Simultaneous Localization And Mapping) package to create a pointcloud map in the .pcd format. For more information, please click here.
Use third-party tools such as TIER IV's Vector Map Builder to create a Lanelet2 format .osm file.
This section briefly explains how to run your vehicle with Autoware.
"},{"location":"how-to-guides/integrating-autoware/overview/#install-autoware","title":"Install Autoware","text":"Follow the installation steps of Autoware.
"},{"location":"how-to-guides/integrating-autoware/overview/#launch-autoware","title":"Launch Autoware","text":"Launch Autoware with the following command:
ros2 launch autoware_launch autoware.launch.xml vehicle_model:=YOUR_VEHICLE sensor_kit:=YOUR_SENSOR_KIT map_path:=/PATH/TO/YOUR/MAP\nIf GNSS is available, Autoware automatically initializes the vehicle's pose.
If not, you need to set the initial pose using the RViz GUI.
Set a goal pose for the ego vehicle.
In your terminal, execute the following command.
source ~/autoware.YOURS/install/setup.bash\nros2 topic pub /autoware.YOURS/engage autoware_auto_vehicle_msgs/msg/Engage \"engage: true\" -1\nYou can also engage via RViz with \"AutowareStatePanel\". The panel can be found in Panels > Add New Panel > tier4_state_rviz_plugin > AutowareStatePanel.
Now the vehicle should drive along the calculated path!
"},{"location":"how-to-guides/integrating-autoware/overview/#6-tune-parameters-for-your-vehicle-environment","title":"6. Tune parameters for your vehicle & environment","text":"You may need to tune your parameters depending on the domain in which you will operate your vehicle.
"},{"location":"how-to-guides/integrating-autoware/creating-maps/","title":"Creating maps","text":""},{"location":"how-to-guides/integrating-autoware/creating-maps/#creating-maps","title":"Creating maps","text":"Autoware requires a pointcloud map and a vector map for the vehicle's operating environment. (Check the map design documentation page for the detailed specification).
This page explains how users can create maps that can be used for Autoware.
"},{"location":"how-to-guides/integrating-autoware/creating-maps/#creating-a-point-cloud-map","title":"Creating a point cloud map","text":"Traditionally, a Mobile Mapping System (MMS) is used in order to create highly accurate large-scale point cloud maps. However, since a MMS requires high-end sensors for precise positioning, its operational cost can be very expensive and may not be suitable for a relatively small driving environment. Alternatively, a Simultaneous Localization And Mapping (SLAM) algorithm can be used to create a point cloud map from recorded LiDAR scans. Some of the useful open-source SLAM implementations are listed in this page.
If you prefer proprietary software that is easy to use, you can try a fully automatic mapping tool from MAP IV, Inc., MapIV Engine. They currently provide a trial license for Autoware users free of charge.
"},{"location":"how-to-guides/integrating-autoware/creating-maps/#creating-a-vector-map","title":"Creating a vector map","text":"The easiest way to create an Autoware-compatible vector map is to use Vector Map Builder, a free web-based tool provided by TIER IV, Inc.. Vector Map Builder allows you to create lanes and add additional regulatory elements such as stop signs or traffic lights using a point cloud map as a reference.
For open-source software options, MapToolbox is a plugin for Unity specifically designed to create Lanelet2 maps for Autoware. Although JOSM is another open-source tool that can be used to create Lanelet2 maps, be aware that a number of modifications must be done manually to make the map compatible with Autoware. This process can be tedious and time-consuming, so the use of JOSM is not recommended.
"},{"location":"how-to-guides/integrating-autoware/creating-maps/#autoware-compatible-map-providers","title":"Autoware-compatible map providers","text":"If it is not possible to create HD maps yourself, you can use a mapping service from the following Autoware-compatible map providers instead:
The table below shows each company's mapping technology and the types of HD maps they support.
Company Mapping technology Available maps MAP IV, Inc. SLAM Point cloud and vector maps AISAN TECHNOLOGY CO., LTD. MMS Point cloud and vector maps TomTom MMS Vector map*Note
Maps provided by TomTom use their proprietary AutoStream format, not Lanelet2. The open-source AutoStreamForAutoware tool can be used to convert an AutoStream map to a Lanelet2 map. However, the converter is still in its early stages and has some known limitations.
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/","title":"Available Open Source SLAM","text":""},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/#available-open-source-slam","title":"Available Open Source SLAM","text":"This page provides the list of available open source Simultaneous Localization And Mapping (SLAM) implementation that can be used to generete a point cloud (.pcd) map file.
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/#selecting-which-implementation-to-use","title":"Selecting which implementation to use","text":"Lidar odometry drifts accumulatively as time goes by and there is solutions to solve that problem such as graph optimization, loop closure and using gps sensor to decrease accumulative drift error. Because of that, a SLAM algorithm should have loop closure feature, graph optimization and should use gps sensor. Additionally, some of the algorithms are using IMU sensor to add another factor to graph for decreasing drift error. While some of the algorithms requires 9-axis IMU sensor strictly, some of them requires only 6-axis IMU sensor or not even using the IMU sensor. Before choosing an algorithm to create maps for Autoware please consider these factors depends on your sensor setup or expected quality of generated map.
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/#tips","title":"Tips","text":"Commonly used open-source SLAM implementations are lidarslam-ros2 (LiDAR, IMU*) and LIO-SAM (LiDAR, IMU, GNSS). The required sensor data for each algorithm is specified in the parentheses, where an asterisk (*) indicates that such sensor data is optional. For supported LiDAR models, please check the Github repository of each algorithm. While these ROS 2-based SLAM implementations can be easily installed and used directly on the same machine that runs Autoware, it is important to note that they may not be as well-tested or as mature as ROS 1-based alternatives.
The notable open-source SLAM implementations that are based on ROS 1 include hdl-graph-slam (LiDAR, IMU*, GNSS*), LeGO-LOAM (LiDAR, IMU*), LeGO-LOAM-BOR (LiDAR), and LIO-SAM (LiDAR, IMU, GNSS).
Most of these algorithms already have a built-in loop-closure and pose graph optimization. However, if the built-in, automatic loop-closure fails or does not work correctly, you can use Interactive SLAM to adjust and optimize a pose graph manually.
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/#list-of-third-party-slam-implementations","title":"List of Third Party SLAM Implementations","text":"Package Name Explanation Repository Link Loop Closure Sensors ROS Version Dependencies FAST-LIO-LC A computationally efficient and robust LiDAR-inertial odometry package with loop closure module and graph optimization https://github.com/yanliang-wang/FAST_LIO_LC ✓ LidarIMUGPS [Optional] ROS1 ROS MelodicPCL >= 1.8Eigen >= 3.3.4GTSAM >= 4.0.0 FAST_LIO_SLAM FAST_LIO_SLAM is the integration of FAST_LIO and SC-PGO which is scan context based loop detection and GTSAM based pose-graph optimization https://github.com/gisbi-kim/FAST_LIO_SLAM ✓ LidarIMUGPS [Optional] ROS1 PCL >= 1.8Eigen >= 3.3.4 FD-SLAM FD_SLAM is Feature&Distribution-based 3D LiDAR SLAM method based on Surface Representation Refinement. In this algorithm novel feature-based Lidar odometry used for fast scan-matching, and used a proposed UGICP method for keyframe matching https://github.com/SLAMWang/FD-SLAM ✓ LidarIMU [Optional]GPS ROS1 PCLg2oSuitesparse hdl_graph_slam An open source ROS package for real-time 6DOF SLAM using a 3D LIDAR. It is based on 3D Graph SLAM with NDT scan matching-based odometry estimation and loop detection. It also supports several graph constraints, such as GPS, IMU acceleration (gravity vector), IMU orientation (magnetic sensor), and floor plane (detected in a point cloud) https://github.com/koide3/hdl_graph_slam ✓ LidarIMU [Optional]GPS [Optional] ROS1 PCLg2oOpenMP IA-LIO-SAM IA_LIO_SLAM is created for data acquisition in unstructured environment and it is a framework for Intensity and Ambient Enhanced Lidar Inertial Odometry via Smoothing and Mapping that achieves highly accurate robot trajectories and mapping https://github.com/minwoo0611/IA_LIO_SAM ✓ LidarIMUGPS ROS1 GTSAM ISCLOAM ISCLOAM presents a robust loop closure detection approach by integrating both geometry and intensity information https://github.com/wh200720041/iscloam ✓ Lidar ROS1 Ubuntu 18.04ROS MelodicCeresPCLGTSAMOpenCV LeGO-LOAM-BOR LeGO-LOAM-BOR is improved version of the LeGO-LOAM by improving quality of the code, making it more readable and consistent. Also, performance is improved by converting processes to multi-threaded approach https://github.com/facontidavide/LeGO-LOAM-BOR ✓ LidarIMU ROS1 ROS MelodicPCLGTSAM LIO_SAM A framework that achieves highly accurate, real-time mobile robot trajectory estimation and map-building. It formulates lidar-inertial odometry atop a factor graph, allowing a multitude of relative and absolute measurements, including loop closures, to be incorporated from different sources as factors into the system https://github.com/TixiaoShan/LIO-SAM ✓ LidarIMUGPS [Optional] ROS1ROS2 PCLGTSAM Optimized-SC-F-LOAM An improved version of F-LOAM and uses an adaptive threshold to further judge the loop closure detection results and reducing false loop closure detections. Also it uses feature point-based matching to calculate the constraints between a pair of loop closure frame point clouds and decreases time consumption of constructing loop frame constraints https://github.com/SlamCabbage/Optimized-SC-F-LOAM ✓ Lidar ROS1 PCLGTSAMCeres SC-A-LOAM A real-time LiDAR SLAM package that integrates A-LOAM and ScanContext. https://github.com/gisbi-kim/SC-A-LOAM ✓ Lidar ROS1 GTSAM >= 4.0 SC-LeGO-LOAM SC-LeGO-LOAM integrated LeGO-LOAM for lidar odometry and 2 different loop closure methods: ScanContext and Radius search based loop closure. While ScanContext is correcting large drifts, radius search based method is good for fine-stitching https://github.com/irapkaist/SC-LeGO-LOAM ✓ LidarIMU ROS1 PCLGTSAM"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/fast-lio-lc/","title":"FAST_LIO_LC","text":""},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/fast-lio-lc/#fast_lio_lc","title":"FAST_LIO_LC","text":""},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/fast-lio-lc/#what-is-fast_lio_lc","title":"What is FAST_LIO_LC?","text":"https://github.com/yanliang-wang/FAST_LIO_LC
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/fast-lio-lc/#required-sensors","title":"Required Sensors","text":" wget -O ~/Downloads/gtsam.zip https://github.com/borglab/gtsam/archive/4.0.0-alpha2.zip\n cd ~/Downloads/ && unzip gtsam.zip -d ~/Downloads/\n cd ~/Downloads/gtsam-4.0.0-alpha2/\n mkdir build && cd build\n cmake ..\n sudo make install\n mkdir -p ~/ws_fastlio_lc/src\n cd ~/ws_fastlio_lc/src\n git clone https://github.com/gisbi-kim/FAST_LIO_SLAM.git\n git clone https://github.com/Livox-SDK/livox_ros_driver\n cd ..\n catkin_make\nworkspace/src/FAST_LIO_LC/FAST_LIO/config/ouster64_mulran.yaml) with the lidar topic name in your bag file.pcd_save_enable must be 1 from the launch file (workspace/src/FAST_LIO_LC/FAST_LIO/launch/mapping_ouster64_mulran.launch).# open new terminal: run FAST-LIO\nroslaunch fast_lio mapping_ouster64.launch\n\n# open the other terminal tab: run SC-PGO\nroslaunch aloam_velodyne fastlio_ouster64.launch\n\n# play bag file in the other terminal\nrosbag play RECORDED_BAG.bag --clock\nCheck original repository link for example dataset.
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/fast-lio-lc/#contact","title":"Contact","text":"wyl410922@qq.com)https://github.com/gisbi-kim/FAST_LIO_SLAM
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/fast-lio-slam/#required-sensors","title":"Required Sensors","text":"wget -O ~/Downloads/gtsam.zip https://github.com/borglab/gtsam/archive/4.0.0-alpha2.zip\ncd ~/Downloads/ && unzip gtsam.zip -d ~/Downloads/\ncd ~/Downloads/gtsam-4.0.0-alpha2/\nmkdir build && cd build\ncmake ..\nsudo make install\n mkdir -p ~/catkin_fastlio_slam/src\n cd ~/catkin_fastlio_slam/src\n git clone https://github.com/gisbi-kim/FAST_LIO_SLAM.git\n git clone https://github.com/Livox-SDK/livox_ros_driver\n cd ..\n catkin_make\n source devel/setup.bash\nFast_LIO/config/ouster64.yaml # terminal 1: run FAST-LIO2\nroslaunch fast_lio mapping_ouster64.launch\n\n # open the other terminal tab: run SC-PGO\ncd ~/catkin_fastlio_slam\n source devel/setup.bash\n roslaunch aloam_velodyne fastlio_ouster64.launch\n\n # play bag file in the other terminal\nrosbag play xxx.bag -- clock --pause\nTutorial video 1 (using KAIST 03 sequence of MulRan dataset)
paulgkim@kaist.ac.kr)This is an open source ROS package for real-time 6DOF SLAM using a 3D LIDAR.
It is based on hdl_graph_slam and the steps to run our system are same with hdl-graph-slam.
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/fd-slam/#original-repository-link","title":"Original Repository link","text":"https://github.com/SLAMWang/FD-SLAM
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/fd-slam/#required-sensors","title":"Required Sensors","text":"The following ROS packages are required:
cd ~/catkin_ws/src\ngit clone https://github.com/SLAMWang/FD-SLAM.git\ncd ..\ncatkin_make\n/hdl_graph_slam/dump (hdl_graph_slam/DumpGraph)\n- save all the internal data (point clouds, floor coeffs, odoms, and pose graph) to a directory.\n\n/hdl_graph_slam/save_map (hdl_graph_slam/SaveMap)\n- save the generated map as a PCD file.\nsource devel/setup.bash\nroslaunch hdl_graph_slam hdl_graph_slam_400_ours.launch\nhttps://github.com/koide3/hdl_graph_slam
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/hdl-graph-slam/#required-sensors","title":"Required Sensors","text":"The following ROS packages are required:
# for melodic\nsudo apt-get install ros-melodic-geodesy ros-melodic-pcl-ros ros-melodic-nmea-msgs ros-melodic-libg2o\ncd catkin_ws/src\ngit clone https://github.com/koide3/ndt_omp.git -b melodic\ngit clone https://github.com/SMRT-AIST/fast_gicp.git --recursive\ngit clone https://github.com/koide3/hdl_graph_slam\n\ncd .. && catkin_make -DCMAKE_BUILD_TYPE=Release\n\n# for noetic\nsudo apt-get install ros-noetic-geodesy ros-noetic-pcl-ros ros-noetic-nmea-msgs ros-noetic-libg2o\n\ncd catkin_ws/src\ngit clone https://github.com/koide3/ndt_omp.git\ngit clone https://github.com/SMRT-AIST/fast_gicp.git --recursive\ngit clone https://github.com/koide3/hdl_graph_slam\n\ncd .. && catkin_make -DCMAKE_BUILD_TYPE=Release\nlaunch/hdl_graph_slam_400.launchlaunch/hdl_graph_slam_400.launchrosparam set use_sim_time true\nroslaunch hdl_graph_slam hdl_graph_slam_400.launch\nroscd hdl_graph_slam/rviz\nrviz -d hdl_graph_slam.rviz\nrosbag play --clock hdl_400.bag\nSave the generated map by:
rosservice call /hdl_graph_slam/save_map \"resolution: 0.05\ndestination: '/full_path_directory/map.pcd'\"\nBag file (recorded in an outdoor environment):
rosparam set use_sim_time true\nroslaunch hdl_graph_slam hdl_graph_slam_400.launch\nroscd hdl_graph_slam/rviz\nrviz -d hdl_graph_slam.rviz\nrosbag play --clock dataset.bag\n
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/hdl-graph-slam/#papers","title":"Papers","text":"
Kenji Koide, Jun Miura, and Emanuele Menegatti, A Portable 3D LIDAR-based System for Long-term and Wide-area People Behavior Measurement, Advanced Robotic Systems, 2019 [link].
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/hdl-graph-slam/#contact","title":"Contact","text":"Kenji Koide, k.koide@aist.go.jp, https://staff.aist.go.jp/k.koide
[Active Intelligent Systems Laboratory, Toyohashi University of Technology, Japan] [Mobile Robotics Research Team, National Institute of Advanced Industrial Science and Technology (AIST), Japan]
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/ia-lio-slam/","title":"IA-LIO-SAM","text":""},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/ia-lio-slam/#ia-lio-sam","title":"IA-LIO-SAM","text":""},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/ia-lio-slam/#what-is-ia-lio-sam","title":"What is IA-LIO-SAM?","text":"https://github.com/minwoo0611/IA_LIO_SAM
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/ia-lio-slam/#required-sensors","title":"Required Sensors","text":"ROS (tested with Kinetic and Melodic)
for ROS melodic:
sudo apt-get install -y ros-melodic-navigation\nsudo apt-get install -y ros-melodic-robot-localization\nsudo apt-get install -y ros-melodic-robot-state-publisher\nfor ROS kinetic:
sudo apt-get install -y ros-kinetic-navigation\nsudo apt-get install -y ros-kinetic-robot-localization\nsudo apt-get install -y ros-kinetic-robot-state-publisher\nGTSAM (Georgia Tech Smoothing and Mapping library)
wget -O ~/Downloads/gtsam.zip https://github.com/borglab/gtsam/archive/4.0.2.zip\ncd ~/Downloads/ && unzip gtsam.zip -d ~/Downloads/\ncd ~/Downloads/gtsam-4.0.2/\nmkdir build && cd build\ncmake -DGTSAM_BUILD_WITH_MARCH_NATIVE=OFF ..\nsudo make install -j8\n mkdir -p ~/catkin_ia_lio/src\n cd ~/catkin_ia_lio/src\n git clone https://github.com/minwoo0611/IA_LIO_SAM\n cd ..\n catkin_make\nworkspace/src/IA_LIO_SAM/config/params.yaml)savePCD must be true on the params.yaml file (workspace/src/IA_LIO_SAM/config/params.yaml). # open new terminal: run IA_LIO\n source devel/setup.bash\n roslaunch lio_sam mapping_ouster64.launch\n\n # play bag file in the other terminal\n rosbag play RECORDED_BAG.bag --clock\nCheck original repo link for example dataset.
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/ia-lio-slam/#contact","title":"Contact","text":"Github: minwoo0611)Thank you for citing IA-LIO-SAM(./config/doc/KRS-2021-17.pdf) if you use any of this code.
Part of the code is adapted from LIO-SAM (IROS-2020).
@inproceedings{legoloam2018shan,\n title={LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain},\n author={Shan, Tixiao and Englot, Brendan},\n booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},\n pages={4758-4765},\n year={2018},\n organization={IEEE}\n}\nhttps://github.com/wh200720041/iscloam
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/iscloam/#required-sensors","title":"Required Sensors","text":"For visualization purpose, this package uses hector trajectory sever, you may install the package by
sudo apt-get install ros-melodic-hector-trajectory-server\ncd ~/catkin_ws/src\ngit clone https://github.com/wh200720041/iscloam.git\ncd ..\ncatkin_make -j1\nsource ~/catkin_ws/devel/setup.bash\nChange the bag location and sensor parameters on launch files.
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/iscloam/#3-launch","title":"3. Launch","text":"roslaunch iscloam iscloam.launch\nif you would like to generate the map of environment at the same time, you can run
roslaunch iscloam iscloam_mapping.launch\nNote that the global map can be very large, so it may takes a while to perform global optimization, some lag is expected between trajectory and map since they are running in separate thread. More CPU usage will happen when loop closure is identified.
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/iscloam/#example-result","title":"Example Result","text":"Watch demo video at Video Link
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/iscloam/#ground-truth-comparison","title":"Ground Truth Comparison","text":"Green: ISCLOAM Red: Ground Truth
KITTI sequence 00 KITTI sequence 05\nIf you use this work for your research, you may want to cite the paper below, your citation will be appreciated
@inproceedings{wang2020intensity,\n author={H. {Wang} and C. {Wang} and L. {Xie}},\n booktitle={2020 IEEE International Conference on Robotics and Automation (ICRA)},\n title={Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection},\n year={2020},\n volume={},\n number={},\n pages={2095-2101},\n doi={10.1109/ICRA40945.2020.9196764}\n}\nThanks for A-LOAM and LOAM(J. Zhang and S. Singh. LOAM: Lidar Odometry and Mapping in Real-time) and LOAM_NOTED.
Author: Wang Han, Nanyang Technological University, Singapore
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/lego-loam-bor/","title":"LeGO-LOAM-BOR","text":""},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/lego-loam-bor/#lego-loam-bor","title":"LeGO-LOAM-BOR","text":""},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/lego-loam-bor/#what-is-lego-loam-bor","title":"What is LeGO-LOAM-BOR?","text":"https://github.com/facontidavide/LeGO-LOAM-BOR
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/lego-loam-bor/#required-sensors","title":"Required Sensors","text":"wget -O ~/Downloads/gtsam.zip https://github.com/borglab/gtsam/archive/4.0.0-alpha2.zip\ncd ~/Downloads/ && unzip gtsam.zip -d ~/Downloads/\ncd ~/Downloads/gtsam-4.0.0-alpha2/\nmkdir build && cd build\ncmake ..\nsudo make install\ncd ~/catkin_ws/src\ngit clone https://github.com/facontidavide/LeGO-LOAM-BOR.git\ncd ..\ncatkin_make\nLeGo-LOAM/loam_config.yamlsource devel/setup.bash\nroslaunch lego_loam_bor run.launch rosbag:=/path/to/your/rosbag lidar_topic:=/velodyne_points\nThank you for citing our LeGO-LOAM paper if you use any of this code:
@inproceedings{legoloam2018,\n title={LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain},\n author={Tixiao Shan and Brendan Englot},\n booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},\n pages={4758-4765},\n year={2018},\n organization={IEEE}\n}\nhttps://github.com/TixiaoShan/LIO-SAM
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/lio-sam/#required-sensors","title":"Required Sensors","text":"Gtsam (Georgia Tech Smoothing and Mapping library)
sudo add-apt-repository ppa:borglab/gtsam-release-4.0\nsudo apt install libgtsam-dev libgtsam-unstable-dev\n sudo apt-get install -y ros-melodic-navigation\n sudo apt-get install -y ros-melodic-robot-localization\n sudo apt-get install -y ros-melodic-robot-state-publisher\n mkdir -p ~/catkin_lio_sam/src\n cd ~/catkin_lio_sam/src\n git clone https://github.com/TixiaoShan/LIO-SAM.git\n cd ..\n catkin_make\n source devel/setup.bash\nlio_sam/config/params.yaml # Run the Launch File\nroslaunch lio_sam run.launch\n\n # Play bag file in the other terminal\nrosbag play xxx.bag --clock\nThank you for citing LIO-SAM (IROS-2020) if you use any of this code.
@inproceedings{liosam2020shan,\n title={LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping},\n author={Shan, Tixiao and Englot, Brendan and Meyers, Drew and Wang, Wei and Ratti, Carlo and Rus Daniela},\n booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},\n pages={5135-5142},\n year={2020},\n organization={IEEE}\n}\nPart of the code is adapted from LeGO-LOAM.
@inproceedings{legoloam2018shan,\n title={LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain},\n author={Shan, Tixiao and Englot, Brendan},\n booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},\n pages={4758-4765},\n year={2018},\n organization={IEEE}\n}\nhttps://github.com/SlamCabbage/Optimized-SC-F-LOAM
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/optimized-sc-f-loam/#required-sensors","title":"Required Sensors","text":"sudo apt-get install ros-noetic-hector-trajectory-server\ncd ~/catkin_ws/src\ngit clone https://github.com/SlamCabbage/Optimized-SC-F-LOAM.git\ncd ..\ncatkin_make\nIn this folder, Ground Truth information, optimized pose information, F-LOAM pose information and time information are stored
mkdir -p ~/message/Scans\n\nChange line 383 in the laserLoopOptimizationNode.cpp to your own \"message\" folder path\n(Do not forget to rebuild your package)
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/optimized-sc-f-loam/#3-set-parameters","title":"3) Set parameters","text":"source devel/setup.bash\nroslaunch optimized_sc_f_loam optimized_sc_f_loam_mapping.launch\nTest on KITTI sequence You can download the sequence 00 and 05 datasets from the KITTI official website and convert them into bag files using the kitti2bag open source method.
00: 2011_10_03_drive_0027 000000 004540
05: 2011_09_30_drive_0018 000000 002760
See the link: https://github.com/ethz-asl/kitti_to_rosbag
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/optimized-sc-f-loam/#acknowledgements","title":"Acknowledgements","text":"Thanks for SC-A-LOAM(Scan context: Egocentric spatial descriptor for place recognition within 3d point cloud map) and F-LOAM(F-LOAM : Fast LiDAR Odometry and Mapping).
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/optimized-sc-f-loam/#citation","title":"Citation","text":"@misc{https://doi.org/10.48550/arxiv.2204.04932,\n doi = {10.48550/ARXIV.2204.04932},\n\n url = {https://arxiv.org/abs/2204.04932},\n\n author = {Liao, Lizhou and Fu, Chunyun and Feng, Binbin and Su, Tian},\n\n keywords = {Robotics (cs.RO), FOS: Computer and information sciences, FOS: Computer and information sciences},\n\n title = {Optimized SC-F-LOAM: Optimized Fast LiDAR Odometry and Mapping Using Scan Context},\n\n publisher = {arXiv},\n\n year = {2022},\n\n copyright = {arXiv.org perpetual, non-exclusive license}\n}\nhttps://github.com/gisbi-kim/SC-A-LOAM
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/sc-a-loam/#required-sensors","title":"Required Sensors","text":"If GTSAM is not installed, follow the steps below.
wget -O ~/Downloads/gtsam.zip https://github.com/borglab/gtsam/archive/4.0.2.zip\n cd ~/Downloads/ && unzip gtsam.zip -d ~/Downloads/\n cd ~/Downloads/gtsam-4.0.2/\n mkdir build && cd build\n cmake -DGTSAM_BUILD_WITH_MARCH_NATIVE=OFF ..\n sudo make install -j8\nFirst, install the abovementioned dependencies and follow below lines.
mkdir -p ~/catkin_scaloam_ws/src\n cd ~/catkin_scaloam_ws/src\n git clone https://github.com/gisbi-kim/SC-A-LOAM.git\n cd ../\n catkin_make\n source ~/catkin_scaloam_ws/devel/setup.bash\nroslaunch aloam_velodyne aloam_mulran.launch\n rosrun pcl_ros pointcloud_to_pcd input:=/aft_pgo_map\nexample videos on Riverside 01 sequence.
1. with consumer level GPS-based altitude stabilization: https://youtu.be/FwAVX5TVm04\n2. without the z stabilization: https://youtu.be/okML_zNadhY\nexample result:
For KITTI (HDL-64 sensor), run using the command
roslaunch aloam_velodyne aloam_velodyne_HDL_64.launch # for KITTI dataset setting\nexample result:
https://github.com/irapkaist/SC-LeGO-LOAM
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/sc-lego-loam/#required-sensors","title":"Required Sensors","text":"wget -O ~/Downloads/gtsam.zip https://github.com/borglab/gtsam/archive/4.0.0-alpha2.zip\ncd ~/Downloads/ && unzip gtsam.zip -d ~/Downloads/\ncd ~/Downloads/gtsam-4.0.0-alpha2/\nmkdir build && cd build\ncmake ..\nsudo make install\ncd ~/catkin_ws/src\ngit clone https://github.com/irapkaist/SC-LeGO-LOAM.git\ncd ..\ncatkin_make\ninclude/utility.hinclude/utility.hinclude/Scancontext.h(Do not forget to rebuild after setting parameters.)
"},{"location":"how-to-guides/integrating-autoware/creating-maps/open-source-slam/sc-lego-loam/#3-run","title":"3) Run","text":"source devel/setup.bash\nroslaunch lego_loam run.launch\n@INPROCEEDINGS { gkim-2018-iros,\n author = {Kim, Giseop and Kim, Ayoung},\n title = { Scan Context: Egocentric Spatial Descriptor for Place Recognition within {3D} Point Cloud Map },\n booktitle = { Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems },\n year = { 2018 },\n month = { Oct. },\n address = { Madrid }\n}\nand
@inproceedings{legoloam2018,\n title={LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain},\n author={Shan, Tixiao and Englot, Brendan},\n booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},\n pages={4758-4765},\n year={2018},\n organization={IEEE}\n}\npaulgkim@kaist.ac.kr)Autoware expects to have multiple sensors attached to the vehicle as input to perception, localization, and planning stack. These sensors must be calibrated correctly and their positions must be defined using either urdf files (as in sample_sensor_kit) or as tf launch files.
"},{"location":"how-to-guides/integrating-autoware/creating-vehicle-and-sensor-description/calibrating-sensors/#camera-calibration","title":"Camera calibration","text":""},{"location":"how-to-guides/integrating-autoware/creating-vehicle-and-sensor-description/calibrating-sensors/#intrinsic-calibration","title":"Intrinsic Calibration","text":"LL-Calib on Github, provided by AutoCore, is a lightweight toolkit for online/offline 3D LiDAR to LiDAR calibration. It's based on local mapping and \"GICP\" method to derive the relation between main and sub lidar. Information on how to use the tool, troubleshooting tips and example rosbags can be found at the above link.
"},{"location":"how-to-guides/integrating-autoware/creating-vehicle-and-sensor-description/calibrating-sensors/#lidar-camera-calibration","title":"Lidar-camera calibration","text":"Developed by MathWorks, The Lidar Camera Calibrator app enables you to interactively estimate the rigid transformation between a lidar sensor and a camera.
https://ww2.mathworks.cn/help/lidar/ug/get-started-lidar-camera-calibrator.html
SensorsCalibration toolbox v0.1: One more open source method for Lidar-camera calibration. This is a project for LiDAR to camera calibration,including automatic calibration and manual calibration
https://github.com/PJLab-ADG/SensorsCalibration/blob/master/lidar2camera/README.md
Developed by AutoCore, an easy-to-use lightweight toolkit for Lidar-camera-calibration is proposed. Only in three steps, a fully automatic calibration will be done.
https://github.com/autocore-ai/calibration_tools/tree/main/lidar-cam-calib-related
"},{"location":"how-to-guides/integrating-autoware/creating-vehicle-and-sensor-description/calibrating-sensors/#lidar-imu-calibration","title":"Lidar-IMU calibration","text":"Developed by APRIL Lab at Zhejiang University in China, the LI-Calib calibration tool is a toolkit for calibrating the 6DoF rigid transformation and the time offset between a 3D LiDAR and an IMU, based on continuous-time batch optimization. IMU-based cost and LiDAR point-to-surfel (surfel = surface element) distance are minimized jointly, which renders the calibration problem well-constrained in general scenarios.
AutoCore has forked the original LI-Calib tool and overwritten the Lidar input for more general usage. Information on how to use the tool, troubleshooting tips and example rosbags can be found at the LI-Calib fork on Github.
"},{"location":"how-to-guides/integrating-autoware/creating-vehicle-and-sensor-description/creating-vehicle-and-sensor-description/","title":"Creating vehicle and sensor description","text":""},{"location":"how-to-guides/integrating-autoware/creating-vehicle-and-sensor-description/creating-vehicle-and-sensor-description/#creating-vehicle-and-sensor-description","title":"Creating vehicle and sensor description","text":"Warning
Under Construction
"},{"location":"how-to-guides/integrating-autoware/creating-vehicle-interface-package/creating-vehicle-interface-for-ackerman-kinematic-model/","title":"Creating vehicle interface for ackerman kinematic model","text":""},{"location":"how-to-guides/integrating-autoware/creating-vehicle-interface-package/creating-vehicle-interface-for-ackerman-kinematic-model/#creating-vehicle-interface-for-ackerman-kinematic-model","title":"Creating vehicle interface for ackerman kinematic model","text":"Warning
Under Construction
"},{"location":"how-to-guides/integrating-autoware/creating-vehicle-interface-package/customizing-for-differential-drive-model/","title":"Customizing for differential drive vehicle","text":""},{"location":"how-to-guides/integrating-autoware/creating-vehicle-interface-package/customizing-for-differential-drive-model/#customizing-for-differential-drive-vehicle","title":"Customizing for differential drive vehicle","text":""},{"location":"how-to-guides/integrating-autoware/creating-vehicle-interface-package/customizing-for-differential-drive-model/#1-introduction","title":"1. Introduction","text":"Currently, Autoware assumes that vehicles use an Ackermann kinematic model with Ackermann steering. Thus, Autoware adopts the Ackermann command format for the Control module's output (see the AckermannDrive ROS message definition for an overview of Ackermann commands, and the AckermannControlCommands struct used in Autoware for more details).
However, it is possible to integrate Autoware with a vehicle that follows a differential drive kinematic model, as commonly used by small mobile robots.
"},{"location":"how-to-guides/integrating-autoware/creating-vehicle-interface-package/customizing-for-differential-drive-model/#2-procedure","title":"2. Procedure","text":"One simple way of using Autoware with a differential drive vehicle is to create a vehicle_interface package that translates Ackermann commands to differential drive commands. Here are two points that you need to consider:
vehicle_interface package for differential drive vehiclewheel_basevehicle_interface package for differential drive vehicle","text":"An Ackermann command in Autoware consists of two main control inputs:
Conversely, a typical differential drive command consists of the following inputs:
So, one way in which an Ackermann command can be converted to a differential drive command is by using the following equations:
v_l = v - \\frac{l\\omega}{2}, v_r = v + \\frac{l\\omega}{2}where l denotes wheel tread.
For information about other factors that need to be considered when creating a vehicle_interface package, refer to the vehicle_interface component page.
wheel_base","text":"A differential drive robot does not necessarily have front and rear wheels, which means that the wheelbase (the horizontal distance between the axles of the front and rear wheels) cannot be defined. However, Autoware expects wheel_base to be set in vehicle_info.param.yaml with some value. Thus, you need to set a pseudo value for wheel_base.
The appropriate pseudo value for wheel_base depends on the size of your vehicle. Setting it to be the same value as wheel_tread is one possible choice.
Warning
wheel_base value is set too small then the vehicle may behave unexpectedly. For example, the vehicle may drive beyond the bounds of a calculated path.wheel_base is set too large, the vehicle's range of motion will be restricted. The reason being that Autoware's Planning module will calculate an overly conservative trajectory based on the assumed vehicle length.Since Autoware assumes that vehicles use a steering system, it is not possible to take advantage of the flexibility of a differential drive system's motion model.
For example, when planning a parking maneuver with the freespace_planner module, Autoware may drive the differential drive vehicle forward and backward, even if the vehicle can be parked with a simpler trajectory that uses pure rotational movement.
This page shows how to use control_performance_analysis package to evaluate the controllers.
control_performance_analysis is the package to analyze the tracking performance of a control module and monitor the driving status of the vehicle.
If you need more detailed information about package, refer to the control_performance_analysis.
"},{"location":"how-to-guides/integrating-autoware/tuning-parameters-and-performance/evaluating-controller-performance/#how-to-use","title":"How to use","text":""},{"location":"how-to-guides/integrating-autoware/tuning-parameters-and-performance/evaluating-controller-performance/#before-driving","title":"Before Driving","text":""},{"location":"how-to-guides/integrating-autoware/tuning-parameters-and-performance/evaluating-controller-performance/#1-firstly-you-need-to-launch-autoware-you-can-also-use-this-tool-with-real-vehicle-driving","title":"1. Firstly you need to launch Autoware. You can also use this tool with real vehicle driving","text":""},{"location":"how-to-guides/integrating-autoware/tuning-parameters-and-performance/evaluating-controller-performance/#2-initialize-the-vehicle-and-send-goal-position-to-create-route","title":"2. Initialize the vehicle and send goal position to create route","text":"ros2 launch control_performance_analysis controller_performance_analysis.launch.xml\nsource ~/autoware/install/setup.bash\nros2 run plotjuggler plotjuggler\n/autoware.universe/control/control_performance_analysis/config/controller_monitor.xml","text":"If present, use the timestamp in the field [header.stamp] box, then select the OK..cvs file from data section.Help -> Cheatsheet in PlotJuggler to see more tips about it.odom_interval and low_pass_filter_gain from here to avoid noised data.Autoware should be real-time system when integrated to a service. Therefore, the response time of each callback should be as small as possible. If Autoware appears to be slow, it is imperative to conduct performance measurements and implement improvements based on the analysis. However, Autoware is a complex software system comprising numerous ROS 2 nodes, potentially complicating the process of identifying bottlenecks. To address this challenge, we will discuss methods for conducting detailed performance measurements for Autoware and provide case studies. It is worth noting that multiple factors can contribute to poor performance, such as scheduling and memory allocation in the OS layer, but our focus in this page will be on user code bottlenecks. The outline of this section is as follows:
Improvement is impossible without precise measurements. To measure the performance of the application code, it is essential to eliminate any external influences. Such influences include interference from the operating system and CPU frequency fluctuations. Scheduling effects also occur when core resources are shared by multiple threads. This section outlines a technique for accurately measuring the performance of the application code for a specific node. Though this section only discusses the case of Linux on Intel CPUs, similar considerations should be made in other environments.
"},{"location":"how-to-guides/integrating-autoware/tuning-parameters-and-performance/evaluating-real-time-performance/#single-node-execution","title":"Single node execution","text":"To eliminate the influence of scheduling, the node being measured should operate independently, using the same logic as when the entire Autoware system is running. To accomplish this, record all input topics of the node to be measured while the whole Autoware system is running. To achieve this objective, a tool called ros2_single_node_replayer has been prepared.
Details on how to use the tool can be found in the README. This tool records the input topics of a specific node during the entire Autoware operation and replays it in a single node with the same logic. The tool relies on the ros2 bag record command, and the recording of service/action is not supported as of ROS 2 Humble, so nodes that use service/action as their main logic may not work well.
Isolated cores running the node to be measured must meet the following conditions.
To fulfill these conditions on Linux, a custom kernel build with the following kernel configurations is required. You can find many resources to instruct you on how to build a custom Linux kernel (like this one). Note that even if full tickless is enabled, timer interrupts are generated for scheduling if more than two tasks exist in one core.
# Enable CONFIG_NO_HZ_FULL\n-> General setup\n-> Timers subsystem\n-> Timer tick handling (Full dynticks system (tickless))\n(X) Full dynticks system (tickless)\n\n# Allows RCU callback processing to be offloaded from selected CPUs\n# (CONFIG_RCU_NOCB_CPU=y)\n-> General setup\n-> RCU Subsystem\n-*- Offload RCU callback processing from boot-selected CPUs\nAdditionally, the kernel boot parameters need to be set as follows.
GRUB_CMDLINE_LINUX_DEFAULT=\n \"... isolcpus=2,8 rcu_nocbs=2,8 rcu_nocb_poll nohz_full=2,8 intel_pstate=disable\u201d\nIn the above configuration, for example, the node to be measured is assumed to run on core 2, and core 8, which is a hyper-threading pair, is also being isolated. Appropriate decisions on which cores to run the measurement target and which nodes to isolate need to be made based on the cache and core layout of the measurement machine. You can easily check if it is properly configured by running cat /proc/softirqs. Since intel_pstate=disable is specified in the kernel boot parameter, userspace can be specified in the scaling governor.
cat /sys/devices/system/cpu/cpu2/cpufreq/scaling_governor // ondemand\nsudo sh -c \"echo userspace > /sys/devices/system/cpu/cpu2/cpufreq/scaling_governor\"\nThis allows you to freely set the desired frequency within a defined range.
sudo sh -c \"echo <freq(kz)> > /sys/devices/system/cpu/cpu2/cpufreq/scaling_setspeed\"\nTurbo Boost needs to be switched off on Intel CPUs, which is often overlooked.
sudo sh -c \"echo 0 > /sys/devices/system/cpu/cpufreq/boost\"\nFollowing the instructions in the ros2_single_node_replayer README, start the node and play the dedicated rosbag created by the tool. Before playing the rosbag, appropriately set the CPU affinity of the thread on which the node runs, so it is placed on the isolated core prepared.
taskset --cpu-list -p <target cpu> <pid>\nTo avoid interference in the last level cache, minimize the number of other applications running during the measurement.
"},{"location":"how-to-guides/integrating-autoware/tuning-parameters-and-performance/evaluating-real-time-performance/#measurement-and-visualization","title":"Measurement and visualization","text":"To visualize the performance of the measurement target, embed code for logging timestamps and performance counter values in the target source code. To achieve this objective, a tool called pmu_analyzer has been prepared.
Details on how to use the tool can be found in the README. This tool can measure the turnaround time of any section in the source code, as well as various performance counters.
"},{"location":"how-to-guides/integrating-autoware/tuning-parameters-and-performance/evaluating-real-time-performance/#case-studies","title":"Case studies","text":"In this section, we will present several case studies that demonstrate the performance improvements. These examples not only showcase our commitment to enhancing the system's efficiency but also serve as a valuable resource for developers who may face similar challenges in their own projects. The performance improvements discussed here span various components of the Autoware system, including sensing modules and planning modules. There are tendencies for each component regarding which points are becoming bottlenecks. By examining the methods, techniques, and tools employed in these case studies, readers can gain a better understanding of the practical aspects of optimizing complex software systems like Autoware.
"},{"location":"how-to-guides/integrating-autoware/tuning-parameters-and-performance/evaluating-real-time-performance/#sensing-component","title":"Sensing component","text":"First, we will explain the procedure for performance improvement, taking the node ring_outlier_filter as an example. Refer to the Pull Request for details.
The following figure is a time-series plot of the turnaround time of the main processing part of ring_outlier_filter, analyzed as described in the \"Performance Measurement\" section above.
The horizontal axis indicates the number of callbacks called (i.e., callback index), and the vertical axis indicates the turnaround time.
When analyzing the performance of the sensing module from the viewpoint of performance counter, pay attention to instructions, LLC-load-misses, LLC-store-misses, cache-misses, and minor-faults.
Analysis of the performance counter shows that the largest fluctuations come from minor-faults (i.e., soft page faults), the second largest from LLC-store-misses and LLC-load-misses (i.e., cache misses in the last level cache), and the slowest fluctuations come from instructions (i.e., message data size fluctuations). For example, when we plot minor-faults on the horizontal axis and turnaround time on the vertical axis, we can see the following dominant proportional relationship.
To achieve zero soft page faults, heap allocations must only be made from areas that have been first touched in advance. We have developed a library called heaphook to avoid soft page faults while running Autoware callback. If you are interested, refer to the GitHub discussion and the issue.
To reduce LLC misses, it is necessary to reduce the working set and to use cache-efficient access patterns.
In the sensing component, which handles large message data such as LiDAR point cloud data, minimizing copying is important. A callback that takes sensor data message types as input and output should be written in an in-place algorithm as much as possible. This means that in the following pseudocode, when generating output_msg from input_msg, it is crucial to avoid using buffers as much as possible to reduce the number of memory copies.
void callback(const PointCloudMsg &input_msg) {\nauto output_msg = allocate_msg<PointCloudMsg>(output_size);\nfill(input_msg, output_msg);\npublish(std::move(output_msg));\n}\nTo improve cache efficiency, implement an in-place style as much as possible, instead of touching memory areas sporadically. In ROS applications using PCL, the code shown below is often seen.
void callback(const sensor_msgs::PointCloud2ConstPtr &input_msg) {\npcl::PointCloud<PointT>::Ptr input_pcl(new pcl::PointCloud<PointT>);\npcl::fromROSMsg(*input_msg, *input_pcl);\n\n// Algorithm is described for point cloud type of pcl\npcl::PointCloud<PointT>::Ptr output_pcl(new pcl::PointCloud<PointT>);\nfill_pcl(*input_pcl, *output_pcl);\n\nauto output_msg = allocate_msg<sensor_msgs::PointCloud2>(output_size);\npcl::toROSMsg(*output_pcl, *output_msg);\npublish(std::move(output_msg));\n}\nTo use the PCL library, fromROSMsg() and toROSMsg() are used to perform message type conversion at the beginning and end of the callback. This is a wasteful copying process and should be avoided. We should eliminate unnecessary type conversions by removing dependencies on PCL (e.g., https://github.com/tier4/velodyne_vls/pull/39). For large message types such as map data, there should be only one instance in the entire system in terms of physical memory.
First, we will pick up detection_area module in behavior_velocity_planner node, which tends to have long turnaround time. We have followed the performance analysis steps above to obtain the following graph. Axises are the same as the graphs in the sensing case study.
Using pmu_analyzer tool to further identify the bottleneck, we have found that the following multiple loops were taking up a lot of processing time:
for ( area : detection_areas )\nfor ( point : point_clouds )\nif ( boost::geometry::within(point, area) )\n// do something with O(1)\nIt checks whether each point cloud is contained in each detection area. Let N be the size of point_clouds and M be the size of detection_areas, then the computational complexity of this program is O(N^2 * M), since the complexity of within is O(N). Here, given that most of the point clouds are located far away from a certain detection area, a certain optimization can be achieved. First, calculate the minimum enclosing circle that completely covers the detection area, and then check whether the points are contained in that circle. Most of the point clouds can be quickly ruled out by this method, we don\u2019t have to call the within function in most cases. Below is the pseudocode after optimization.
for ( area : detection_areas )\ncircle = calc_minimum_enclosing_circle(area)\nfor ( point : point_clouds )\nif ( point is in circle )\nif ( boost::geometry::within(point, area) )\n// do something with O(1)\nBy using O(N) algorithm for minimum enclosing circle, the computational complexity of this program is reduced to almost O(N * (N + M)) (note that the exact computational complexity does not really change). If you are interested, refer to the Pull Request.
Similar to this example, in the planning component, we take into consideration thousands to tens of thousands of point clouds, thousands of points in a path representing our own route, and polygons representing obstacles and detection areas in the surroundings, and we repeatedly create paths based on them. Therefore, we access the contents of the point clouds and paths multiple times using for-loops. In most cases, the bottleneck lies in these naive for-loops. Here, understanding Big O notation and reducing the order of computational complexity directly leads to performance improvements.
"},{"location":"how-to-guides/others/add-a-custom-ros-message/","title":"Add a custom ROS message","text":""},{"location":"how-to-guides/others/add-a-custom-ros-message/#add-a-custom-ros-message","title":"Add a custom ROS message","text":""},{"location":"how-to-guides/others/add-a-custom-ros-message/#overview","title":"Overview","text":"During the Autoware development, you will probably need to define your own messages. Read the following instructions before adding a custom message.
Message in autoware_msgs define interfaces of Autoware Core.
autoware_msgs, they should first create a new discussion post under the Design category.Any other minor or proposal messages used for internal communication within a component(such as planning) should be defined in another repository.
The following is a simple tutorial of adding a message package to autoware_msgs. For the general ROS2 tutorial, see Create custom msg and srv files.
Make sure you are in the Autoware workspace, and then run the following command to create a new package. As an example, let's create a package to define sensor messages.
Create a package
cd ./src/core/autoware_msgs\nros2 pkg create --build-type ament_cmake autoware_sensing_msgs\nCreate custom messages
You should create .msg files and place them in the msg directory.
NOTE: The initial letters of the .msg and .srv files must be capitalized.
As an example, let's make .msg files GnssInsOrientation.msg and GnssInsOrientationStamped.msg to define GNSS/INS orientation messages:
mkdir msg\ncd msg\ntouch GnssInsOrientation.msg\ntouch GnssInsOrientationStamped.msg\nEdit GnssInsOrientation.msg with your editor to be the following content:
geometry_msgs/Quaternion orientation\nfloat32 rmse_rotation_x\nfloat32 rmse_rotation_y\nfloat32 rmse_rotation_z\nIn this case, the custom message uses a message from another message package geometry_msgs/Quaternion.
Edit GnssInsOrientationStamped.msg with your editor to be the following content:
std_msgs/Header header\nGnssInsOrientation orientation\nIn this case, the custom message uses a message from another message package std_msgs/Header.
Edit CMakeLists.txt
In order to use this custom message in C++ or Python languages, we need to add the following lines to CMakeList.txt:
rosidl_generate_interfaces(${PROJECT_NAME}\n\"msg/GnssInsOrientation.msg\"\n\"msg/GnssInsOrientationStamped.msg\"\nDEPENDENCIES\ngeometry_msgs\nstd_msgs\nADD_LINTER_TESTS\n)\n The ament_cmake_auto tool is very useful and is more widely used in Autoware, so we recommend using ament_cmake_auto instead of ament_cmake.
We need to replace
find_package(ament_cmake REQUIRED)\n\nament_package()\nwith
find_package(ament_cmake_auto REQUIRED)\n\nament_auto_package()\nEdit package.xml
We need to declare relevant dependencies in package.xml. For the above example we need to add the following content:
<buildtool_depend>rosidl_default_generators</buildtool_depend>\n\n<exec_depend>rosidl_default_runtime</exec_depend>\n\n<depend>geometry_msgs</depend>\n<depend>std_msgs</depend>\n\n<member_of_group>rosidl_interface_packages</member_of_group>\nWe need to replace <buildtool_depend>ament_cmake</buildtool_depend> with <buildtool_depend>ament_cmake_auto</buildtool_depend> in the package.xml file.
Build the custom message package
You can build the package in the root of your workspace, for example by running the following command:
colcon build --packages-select autoware_sensing_msgs\nNow the GnssInsOrientationStamped message will be discoverable by other packages in Autoware.
You can use the custom messages in Autoware by following these steps:
package.xml.<depend>autoware_sensing_msgs</depend>..hpp file of the relevant message in the code.#include <autoware_sensing_msgs/msg/gnss_ins_orientation_stamped.hpp>.This page shows some advanced and useful usage of colcon. If you need more detailed information, refer to the colcon documentation.
It is important that you always run colcon build from the workspace root because colcon builds only under the current directory. If you have mistakenly built in a wrong directory, run rm -rf build/ install/ log/ to clean the generated files.
colcon overlays workspaces if you have sourced the setup.bash of other workspaces before building a workspace. You should take care of this especially when you have multiple workspaces.
Run echo $COLCON_PREFIX_PATH to check whether workspaces are overlaid. If you find some workspaces are unnecessarily overlaid, remove all built files, restart the terminal to clean environment variables, and re-build the workspace.
For more details about workspace overlaying, refer to the ROS 2 documentation.
colcon sometimes causes errors of because of the old cache. To remove the cache and rebuild the workspace, run the following command:
rm -rf build/ install/\nIn case you know what packages to remove:
rm -rf {build,install}/{package_a,package_b}\nTo just build specified packages:
colcon build --packages-select <package_name1> <package_name2> ...\nTo build specified packages and their dependencies recursively:
colcon build --packages-up-to <package_name1> <package_name2> ...\nYou can also use these options for colcon test.
Set DCMAKE_BUILD_TYPE to change the optimization level.
Warning
If you specify DCMAKE_BUILD_TYPE=Debug or no DCMAKE_BUILD_TYPE is given for building the entire Autoware, it may be too slow to use.
colcon build --cmake-args -DCMAKE_BUILD_TYPE=Debug\ncolcon build --cmake-args -DCMAKE_BUILD_TYPE=RelWithDebInfo\ncolcon build --cmake-args -DCMAKE_BUILD_TYPE=Release\nCreate $COLCON_HOME/defaults.yaml to change the default configuration.
mkdir -p ~/.colcon\ncat << EOS > ~/.colcon/defaults.yaml\n{\n\"build\": {\n\"symlink-install\": true\n}\n}\nFor more details, see here.
"},{"location":"how-to-guides/others/advanced-usage-of-colcon/#generating-compile_commandsjson","title":"Generating compile_commands.json","text":"compile_commands.json is used by IDEs/tools to analyze the build dependencies and symbol relationships.
You can generate it with the flag DCMAKE_EXPORT_COMPILE_COMMANDS=1:
colcon build --cmake-args -DCMAKE_EXPORT_COMPILE_COMMANDS=1\nTo see the compiler and linker invocations for a package, use VERBOSE=1 and --event-handlers console_cohesion+:
VERBOSE=1 colcon build --packages-up-to <package_name> --event-handlers console_cohesion+\nFor other options, see here.
"},{"location":"how-to-guides/others/advanced-usage-of-colcon/#using-ccache","title":"Using Ccache","text":"Ccache can speed up recompilation. It is recommended to use it to save your time unless you have a specific reason not to do so.
Install Ccache:
sudo apt update && sudo apt install ccache\nWrite the following in your .bashrc:
export CC=\"/usr/lib/ccache/gcc\"\nexport CXX=\"/usr/lib/ccache/g++\"\nClang-Tidy is a powerful C++ linter.
"},{"location":"how-to-guides/others/applying-clang-tidy-to-ros-packages/#preparation","title":"Preparation","text":"You need to generate build/compile_commands.json before using Clang-Tidy.
colcon build --cmake-args -DCMAKE_EXPORT_COMPILE_COMMANDS=1\nclang-tidy -p build/ path/to/file1 path/to/file2 ...\nIf you want to apply Clang-Tidy to all files in a package, using the fd command is useful. To install fd, see the installation manual.
clang-tidy -p build/ $(fd -e cpp -e hpp --full-path \"/autoware_utils/\")\nRefer to the CLion Documentation.
"},{"location":"how-to-guides/others/applying-clang-tidy-to-ros-packages/#visual-studio-code","title":"Visual Studio Code","text":"Use either one of the following extensions:
If you encounter clang-diagnostic-error, try installing libomp-dev.
Related: https://github.com/autowarefoundation/autoware-github-actions/pull/172
"},{"location":"how-to-guides/others/debug-autoware/","title":"Debug Autoware","text":""},{"location":"how-to-guides/others/debug-autoware/#debug-autoware","title":"Debug Autoware","text":"This page provides some methods for debugging Autoware.
"},{"location":"how-to-guides/others/debug-autoware/#print-debug-messages","title":"Print debug messages","text":"The essential thing for debug is to print the program information clearly, which can quickly judge the program operation and locate the problem. Autoware uses ROS 2 logging tool to print debug messages, how to design console logging refer to tutorial Console logging.
"},{"location":"how-to-guides/others/debug-autoware/#using-ros-tools-debug-autoware","title":"Using ROS tools debug Autoware","text":""},{"location":"how-to-guides/others/debug-autoware/#using-command-line-tools","title":"Using command line tools","text":"ROS 2 includes a suite of command-line tools for introspecting a ROS 2 system. The main entry point for the tools is the command ros2, which itself has various sub-commands for introspecting and working with nodes, topics, services, and more. How to use the ROS 2 command line tool refer to tutorial CLI tools.
Rviz2 is a port of Rviz to ROS 2. It provides a graphical interface for users to view their robot, sensor data, maps, and more. You can run Rviz2 tool easily by:
rviz2\nWhen Autoware launch the simulators, the Rviz2 tool is opened by default to visualize the autopilot graphic information.
"},{"location":"how-to-guides/others/debug-autoware/#using-rqt-tools","title":"Using rqt tools","text":"RQt is a graphical user interface framework that implements various tools and interfaces in the form of plugins. You can run any RQt tools/plugins easily by:
rqt\nThis GUI allows you to choose any available plugins on your system. You can also run plugins in standalone windows. For example, RQt Console:
ros2 run rqt_console rqt_console\nrqt_graph: view node interaction
In complex applications, it may be helpful to get a visual representation of the ROS node interactions.
ros2 run rqt_graph rqt_graph\nrqt_console: view messages
rqt_console is a great gui for viewing ROS topics.
ros2 run rqt_console rqt_console\nrqt_plot: view data plots
rqt_plot is an easy way to plot ROS data in real time.
ros2 run rqt_plot rqt_plot\nros2_graph can be used to generate mermaid description of ROS 2 graphs to add on your markdown files.
It can also be used as a colorful alternative to rqt_graph even though it would require some tool to render the generated mermaid diagram.
It can be installed with:
pip install ros2-graph\nThen you can generate a mermaid description of the graph with:
ros2_graph your_node\n\n# or like with an output file\nros2_graph /turtlesim -o turtle_diagram.md\n\n# or multiple nodes\nros2_graph /turtlesim /teleop_turtle\nYou can then visualize these graphs with:
When your ROS 2 setup is not running as expected, you can check its settings with the ros2doctor tool.
ros2doctor checks all aspects of ROS 2, including platform, version, network, environment, running systems and more, and warns you about possible errors and reasons for issues.
It's as simple as just running ros2 doctor in your terminal.
It has the ability to list \"Subscribers without publishers\" for all topics in the system.
And this information can help you find if a necessary node isn't running.
For more details, see the following official documentation for Using ros2doctor to identify issues.
"},{"location":"how-to-guides/others/debug-autoware/#using-a-debugger-with-breakpoints","title":"Using a debugger with breakpoints","text":"Many IDE(e.g. Visual Studio Code, CLion) supports debugging C/C++ executable with GBD on linux platform. The following lists some references for using the debugger:
For any developers who wish to try and deploy Autoware as a microservices architecture, it is necessary to understand the software dependencies, communication, and implemented features of each ROS package/node.
As an example, the commands necessary to determine the dependencies for the Perception component are shown below.
"},{"location":"how-to-guides/others/determining-component-dependencies/#perception-component-dependencies","title":"Perception component dependencies","text":"To generate a graph of package dependencies, use the following colcon command:
colcon graph --dot --packages-up-to tier4_perception_launch | dot -Tpng -o graph.png\nTo generate a list of dependencies, use:
colcon list --packages-up-to tier4_perception_launch --names-only\nautoware_auto_geometry_msgs\nautoware_auto_mapping_msgs\nautoware_auto_perception_msgs\nautoware_auto_planning_msgs\nautoware_auto_vehicle_msgs\nautoware_cmake\nautoware_lint_common\nautoware_point_types\ncompare_map_segmentation\ndetected_object_feature_remover\ndetected_object_validation\ndetection_by_tracker\neuclidean_cluster\ngrid_map_cmake_helpers\ngrid_map_core\ngrid_map_cv\ngrid_map_msgs\ngrid_map_pcl\ngrid_map_ros\nground_segmentation\nimage_projection_based_fusion\nimage_transport_decompressor\ninterpolation\nkalman_filter\nlanelet2_extension\nlidar_apollo_instance_segmentation\nmap_based_prediction\nmulti_object_tracker\nmussp\nobject_merger\nobject_range_splitter\noccupancy_grid_map_outlier_filter\npointcloud_preprocessor\npointcloud_to_laserscan\nshape_estimation\ntensorrt_yolo\ntier4_autoware_utils\ntier4_debug_msgs\ntier4_pcl_extensions\ntier4_perception_launch\ntier4_perception_msgs\ntraffic_light_classifier\ntraffic_light_map_based_detector\ntraffic_light_ssd_fine_detector\ntraffic_light_visualization\nvehicle_info_util\nTip
To output a list of modules with their respective paths, run the command above without the --names-only parameter.
To see which ROS topics are being subscribed and published to, use rqt_graph as follows:
ros2 launch tier4_perception_launch perception.launch.xml mode:=lidar\nros2 run rqt_graph rqt_graph\nThis page will show you how to set up Eagleye in order to use it with Autoware. For the details of the integration proposal, please refer to this Discussion.
"},{"location":"how-to-guides/others/eagleye-integration-guide/#what-is-eagleye","title":"What is Eagleye?","text":"Eagleye is an open-source GNSS/IMU-based localizer initially developed by MAP IV. Inc. It provides a cost-effective alternative to LiDAR and point cloud-based localization by using low-cost GNSS and IMU sensors to provide vehicle position, orientation, and altitude information. By integrating Eagleye into Autoware, users can choose between LiDAR and point cloud-based localization stacks or GNSS/IMU-based Eagleye localizer, depending on their specific needs and operating environment.
"},{"location":"how-to-guides/others/eagleye-integration-guide/#architecture","title":"Architecture","text":"Eagleye can be utilized in the Autoware localization stack in two ways:
Feed only twist into the EKF localizer.
Feed both twist and pose from Eagleye into the EKF localizer (twist can also be used with regular gyro_odometry).
Note that RTK positioning is only required for localization using the Eagleye pose. RTK positioning is not required for twist.
"},{"location":"how-to-guides/others/eagleye-integration-guide/#requirements","title":"Requirements","text":"GNSS/IMU/vehicle speed is required for Eagleye input.
"},{"location":"how-to-guides/others/eagleye-integration-guide/#imu-topic","title":"IMU topic","text":"sensor_msgs/msg/Imu are supported for IMU.
geometry_msgs/msg/TwistStamped and geometry_msgs/msg/TwistWithCovarianceStamped are supported for the input vehicle speed.
Eagleye requires latitude/longitude height information and velocity information generated by the GNSS receiver. Your GNSS ROS driver must publish the following messages:
sensor_msgs/msg/NavSatFix: This message contains latitude, longitude, and height information.geometry_msgs/msg/TwistWithCovarianceStamped: This message contains gnss doppler velocity information.Eagleye has been tested with the following example GNSS ROS drivers: ublox_gps and septentrio_gnss_driver. The settings needed for each of these drivers are as follows:
sensor_msgs/msg/NavSatFix and geometry_msgs/msg/TwistWithCovarianceStamped required by Eagleye with default settings. Therefore, no additional settings are required.publish.navsatfix and publish.twist in the config file gnss.yaml to trueClone the following three packages for Eagleye:
You need to install Eagleye-related packages and change Autoware's launcher. Four files are required in the Autoware localization launcher to run Eagleye: eagleye_rt.launch.xml, eagleye_config.yaml, gnss_converter.launch.xml, and fix2pose.launch.xml.
You must correctly specify input topics for GNSS latitude, longitude, and height information, GNSS speed information, IMU information, and vehicle speed information in the eagleye_config.yaml.
# Topic\ntwist:\ntwist_type: 1 # TwistStamped : 0, TwistWithCovarianceStamped: 1\ntwist_topic: /sensing/vehicle_velocity_converter/twist_with_covariance\nimu_topic: /sensing/imu/tamagawa/imu_raw\ngnss:\nvelocity_source_type: 2 # rtklib_msgs/RtklibNav: 0, nmea_msgs/Sentence: 1, ublox_msgs/NavPVT: 2, geometry_msgs/TwistWithCovarianceStamped: 3\nvelocity_source_topic: /sensing/gnss/ublox/navpvt\nllh_source_type: 2 # rtklib_msgs/RtklibNav: 0, nmea_msgs/Sentence: 1, sensor_msgs/NavSatFix: 2\nllh_source_topic: /sensing/gnss/ublox/nav_sat_fix\nAlso, the frequency of GNSS and IMU must be set in eagleye_config.yaml
common:\nimu_rate: 50\ngnss_rate: 5\nThe basic parameters that do not need to be changed except those mentioned above, i.e., topic names and sensors' frequency, are described below here. Additionally, the parameters for converting sensor_msgs/msg/NavSatFix to geometry_msgs/msg/PoseWithCovarianceStamped is listed in fix2pose.yaml.
Please refer to map4_localization_launch in the autoware.universe package and map4_localization_component.launch.xml in autoware_launch package for information on how to modify the localization launch.
Eagleye has a function for position estimation and a function for twist estimation, namely pose_estimator and twist_estimator, respectively.
tier4_localization_launch gyro_odometry ndt_scan_matcher map4_localization_launch/eagleye_twist_localization_launch eagleye_rt(gyro/odom/gnss fusion) ndt_scan_matcher map4_localization_launch/eagleye_pose_twist_localization_launch eagleye_rt(gyro/odom/gnss fusion) eagleye_rt(gyro/odom/gnss fusion) In Autoware, you can set the pose estimator to GNSS by setting pose_estimator_mode:=gnss in map4_localization_component.launch.xml in autoware_launch package. Note that the output position might not appear to be in the point cloud maps if you are using maps that are not properly georeferenced. In the case of a single GNSS antenna, initial position estimation (dynamic initialization) can take several seconds to complete after starting to run in an environment where GNSS positioning is available.
Alternatively, the twist estimator can be set to Eagleye and the pose estimator to NDT by specifying pose_estimator_mode:=lidar in the same launch file. Unlike Eagleye position estimation, Eagleye twist estimation first outputs uncorrected raw values when activated, and then outputs corrected twists as soon as static initialization is complete.
Enable Eagleye in Autoware by switching the localization module in autoware.launch.xml and the pose_estimator_mode parameter in map4_localization_component.launch.xml in autoware.launch.xml.
When using Eagleye, comment out tier4_localization_component.launch.xml and start map4_localization_component.launch.xml in autoware.launch.xml.
<!-- Localization -->\n<group if=\"$(var launch_localization)\">\n<!-- <include file=\"$(find-pkg-share autoware_launch)/launch/components/tier4_localization_component.launch.xml\"/> -->\n<include file=\"$(find-pkg-share autoware_launch)/launch/components/map4_localization_component.launch.xml\"/>\n</group>\nEagleye requires an initialization process for proper operation. Without initialization, the output for twist will be in the raw value, and the pose data will not be available.
The first step is static initialization, which involves allowing the Eagleye to remain stationary for approximately 5 seconds after startup to estimate the yaw-rate offset.
The next step is dynamic initialization, which involves running the Eagleye in a straight line for approximately 30 seconds. This process estimates the scale factor of wheel speed and azimuth angle. Once dynamic initialization is complete, the Eagleye will be able to provide corrected twist and pose data.
"},{"location":"how-to-guides/others/fixing-dependent-package-versions/","title":"Fixing dependent package versions","text":""},{"location":"how-to-guides/others/fixing-dependent-package-versions/#fixing-dependent-package-versions","title":"Fixing dependent package versions","text":"Autoware manages dependent package versions in autoware.repos. For example, let's say you make a branch in autoware.universe and add new features. Suppose you update other dependencies with vcs pull after cutting a branch from autoware.universe. Then the version of autoware.universe you are developing and other dependencies will become inconsistent, and the entire Autoware build will fail. We recommend saving the dependent package versions by executing the following command when starting the development.
vcs export src --exact > my_autoware.repos\nAlthough CUDA installation is recommended to achieve better performance for object detection and traffic light recognition in Autoware Universe, it is possible to run these algorithms without CUDA. The following subsections briefly explain how to run each algorithm in such an environment.
"},{"location":"how-to-guides/others/running-autoware-without-cuda/#running-2d3d-object-detection-without-cuda","title":"Running 2D/3D object detection without CUDA","text":"Autoware Universe's object detection can be run using one of five possible configurations:
lidar_centerpointlidar_apollo_instance_segmentationlidar-apollo + tensorrt_yololidar-centerpoint + tensorrt_yoloeuclidean_clusterOf these five configurations, only the last one (euclidean_cluster) can be run without CUDA. For more details, refer to the euclidean_cluster module's README file.
For traffic light recognition (both detection and classification), there are two modules that require CUDA:
traffic_light_ssd_fine_detectortraffic_light_classifierTo run traffic light detection without CUDA, set enable_fine_detection to false in the traffic light launch file. Doing so disables the traffic_light_ssd_fine_detector such that traffic light detection is handled by the map_based_traffic_light_detector module instead.
To run traffic light classification without CUDA, set use_gpu to false in the traffic light classifier launch file. Doing so will force the traffic_light_classifier to use a different classification algorithm that does not require CUDA or a GPU.
Autoware targets the platforms listed below. It may change in future versions of Autoware.
The Autoware Foundation provides no support on other platforms than those listed below.
"},{"location":"installation/#architecture","title":"Architecture","text":"Info
Autoware is scalable and can be customized to work with distributed or less powerful hardware. The minimum hardware requirements given below are just a general recommendation. However, performance will be improved with more cores, RAM and a higher-spec graphics card or GPU core.
Although GPU is not required to run basic functionality, it is mandatory to enable the following neural network related functions:
For details of how to enable object detection and traffic light detection/classification without a GPU, refer to the Running Autoware without CUDA.
"},{"location":"installation/#installing-autoware","title":"Installing Autoware","text":"There are two ways to set up Autoware. Choose one according to your preference.
If any issues occur during installation, refer to the Support page.
"},{"location":"installation/#1-docker-installation","title":"1. Docker installation","text":"Docker can ensure that all developers in a project have a common, consistent development environment. It is recommended for beginners, casual users, people who are unfamiliar with Ubuntu.
For more information, refer to the Docker installation guide.
"},{"location":"installation/#2-source-installation","title":"2. Source installation","text":"Source installation is for the cases where more granular control of the installation environment is needed. It is recommended for experienced users or people who want to customize their environment. Note that some problems may occur depending on your local environment.
For more information, refer to the source installation guide.
"},{"location":"installation/#installing-related-tools","title":"Installing related tools","text":"Some other tools are required depending on the evaluation you want to do. For example, to run an end-to-end simulation you need to install an appropriate simulator.
For more information, see here.
"},{"location":"installation/#additional-settings-for-developers","title":"Additional settings for developers","text":"There are also tools and settings for developers, such as Shells or IDEs.
For more information, see here.
"},{"location":"installation/additional-settings-for-developers/","title":"Additional settings for developers","text":""},{"location":"installation/additional-settings-for-developers/#additional-settings-for-developers","title":"Additional settings for developers","text":""},{"location":"installation/additional-settings-for-developers/#console-settings-for-ros-2","title":"Console settings for ROS 2","text":""},{"location":"installation/additional-settings-for-developers/#colorizing-logger-output","title":"Colorizing logger output","text":"By default, ROS 2 logger doesn't colorize the output. To colorize it, write the following in your .bashrc:
export RCUTILS_COLORIZED_OUTPUT=1\nBy default, ROS 2 logger doesn't output detailed information such as file name, function name, or line number. To customize it, write the following in your .bashrc:
export RCUTILS_CONSOLE_OUTPUT_FORMAT=\"[{severity} {time}] [{name}]: {message} ({function_name}() at {file_name}:{line_number})\"\nFor more options, see here.
"},{"location":"installation/additional-settings-for-developers/#network-settings-for-ros-2","title":"Network settings for ROS 2","text":"ROS 2 employs DDS, and the configuration of ROS 2 and DDS is described separately. For ROS 2 networking concepts, refer to the official documentation.
"},{"location":"installation/additional-settings-for-developers/#ros-2-network-setting","title":"ROS 2 network setting","text":"ROS 2 multicasts data on the local network by default. Therefore, when you develop in an office, the data flows over the local network of your office. It may cause collisions of packets or increases in network traffic.
To avoid these, there are two options.
Unless you plan to use multiple host computers on the local network, localhost-only communication is recommended. For details, refer to the sections below.
"},{"location":"installation/additional-settings-for-developers/#enabling-localhost-only-communication","title":"Enabling localhost-only communication","text":"Write the following in your .bashrc: For more information, see the ROS 2 documentation.
export ROS_LOCALHOST_ONLY=1\nIf you export ROS_LOCALHOST_ONLY=1, MULTICAST must be enabled at the loopback address. To verify that MULTICAST is enabled, use the following command.
$ ip link show lo\n1: lo: <LOOPBACK,MULTICAST,UP,LOWER_UP> mtu 65536 qdisc noqueue state UNKNOWN mode DEFAULT group default qlen 1000\nIf the word MULTICAST is not printed, use the following command to enable it.
sudo ip link set lo multicast on\nROS 2 uses ROS_DOMAIN_ID to create groups and communicate between machines in the groups. Since all ROS 2 nodes use domain ID 0 by default, it may cause unintended interference.
To avoid it, set a different domain ID for each group in your .bashrc:
# Replace X with the Domain ID you want to use\n# Domain ID should be a number in range [0, 101] (inclusive)\nexport ROS_DOMAIN_ID=X\nAlso confirm that ROS_LOCALHOST_ONLY is 0 by using the following command.
echo $ROS_LOCALHOST_ONLY # If the output is 1, localhost has priority.\nFor more information, see the ROS 2 Documentation.
"},{"location":"installation/additional-settings-for-developers/#dds-settings","title":"DDS settings","text":"Autoware uses DDS for inter-node communication. ROS 2 documentation recommends users to tune DDS to utilize its capability. Especially, receive buffer size is the critical parameter for Autoware. If the parameter is not large enough, Autoware will failed in receiving large data like point cloud or image.
"},{"location":"installation/additional-settings-for-developers/#tuning-dds","title":"Tuning DDS","text":"Unless customized, CycloneDDS is adopted by default. For example, to execute Autoware with CycloneDDS, prepare a config file. A sample config file is given below. Save it as cyclonedds_config.xml.
<?xml version=\"1.0\" encoding=\"UTF-8\" ?>\n<CycloneDDS xmlns=\"https://cdds.io/config\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:schemaLocation=\"https://cdds.io/config https://raw.githubusercontent.com/eclipse-cyclonedds/cyclonedds/master/etc/cyclonedds.xsd\">\n<Domain Id=\"any\">\n<General>\n<Interfaces>\n<NetworkInterface autodetermine=\"true\" priority=\"default\" multicast=\"default\" />\n</Interfaces>\n<AllowMulticast>default</AllowMulticast>\n<MaxMessageSize>65500B</MaxMessageSize>\n</General>\n<Internal>\n<SocketReceiveBufferSize min=\"10MB\"/>\n<Watermarks>\n<WhcHigh>500kB</WhcHigh>\n</Watermarks>\n</Internal>\n</Domain>\n</CycloneDDS>\nThis configuration is mostly taken from Eclipse Cyclone DDS:Run-time configuration documentation. You can see why each value is set as such under the documentation link.
Set the config file path and enlarge the Linux kernel maximum buffer size before launching Autoware.
export CYCLONEDDS_URI=file:///absolute/path/to/cyclonedds_config.xml\nsudo sysctl -w net.core.rmem_max=2147483647\nFor more information, Refer to ROS 2 documentation. Reading user guide for chosen DDS is helpful for more understanding.
"},{"location":"installation/additional-settings-for-developers/#tuning-dds-for-multiple-host-computers-for-advanced-users","title":"Tuning DDS for multiple host computers (for advanced users)","text":"When Autoware runs on multiple host computers, IP Fragmentation should be taken into account. As ROS 2 documentation recommends, parameters for IP Fragmentation should be set as shown in the following example.
sudo sysctl -w net.ipv4.ipfrag_time=3\nsudo sysctl -w net.ipv4.ipfrag_high_thresh=134217728 # (128 MB)\nClone autowarefoundation/autoware and move to the directory.
git clone https://github.com/autowarefoundation/autoware.git\ncd autoware\nYou can install the dependencies either manually or using the provided Ansible script.
Note: Before installing NVIDIA libraries, confirm and agree with the licenses.
Be very careful with this method. Make sure you read and confirmed all the steps in the Ansible configuration before using it.
If you've manually installed the dependencies, you can skip this section.
./setup-dev-env.sh docker\nYou might need to log out and log back to make the current user able to use docker.
"},{"location":"installation/autoware/docker-installation-devel/#how-to-set-up-a-workspace","title":"How to set up a workspace","text":"Warning
Before proceeding, confirm and agree with the NVIDIA Deep Learning Container license. By pulling and using the Autoware Universe images, you accept the terms and conditions of the license.
Create the autoware_map directory for map data later.
mkdir ~/autoware_map\nPull the Docker image
docker pull ghcr.io/autowarefoundation/autoware-universe:latest-cuda\nLaunch a Docker container.
For amd64 architecture computers with NVIDIA GPU:
rocker --nvidia --x11 --user --volume $HOME/autoware --volume $HOME/autoware_map -- ghcr.io/autowarefoundation/autoware-universe:latest-cuda\nIf you want to run container without using NVIDIA GPU, or for arm64 architecture computers:
rocker -e LIBGL_ALWAYS_SOFTWARE=1 --x11 --user --volume $HOME/autoware --volume $HOME/autoware_map -- ghcr.io/autowarefoundation/autoware-universe:latest-cuda\nFor detailed reason could be found here
For more advanced usage, see here.
After that, move to the workspace in the container:
cd autoware\nCreate the src directory and clone repositories into it.
mkdir src\nvcs import src < autoware.repos\nUpdate dependent ROS packages.
The dependency of Autoware may change after the Docker image was created. In that case, you need to run the following commands to update the dependency.
sudo apt update\nrosdep update\nrosdep install -y --from-paths src --ignore-src --rosdistro $ROS_DISTRO\nBuild the workspace.
colcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release\nIf there is any build issue, refer to Troubleshooting.
Update the Docker image.
docker pull ghcr.io/autowarefoundation/autoware-universe:latest-cuda\nLaunch a Docker container.
For amd64 architecture computers:
rocker --nvidia --x11 --user --volume $HOME/autoware -- ghcr.io/autowarefoundation/autoware-universe:latest-cuda\nIf you want to run container without using NVIDIA GPU, or for arm64 architecture computers:
rocker -e LIBGL_ALWAYS_SOFTWARE=1 --x11 --user --volume $HOME/autoware -- ghcr.io/autowarefoundation/autoware-universe:latest-cuda\nUpdate the .repos file.
cd autoware\ngit pull\nUpdate the repositories.
vcs import src < autoware.repos\nvcs pull src\nBuild the workspace.
colcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release\nInstalling dependencies manually
Create the autoware_map directory for map data later.
mkdir ~/autoware_map\nLaunch a Docker container.
rocker --nvidia --x11 --user --volume $HOME/autoware_map -- ghcr.io/autowarefoundation/autoware-universe:humble-latest-prebuilt\nFor more advanced usage, see here.
Run Autoware simulator
Inside the container, you can run the Autoware simulation by following this tutorial:
planning simulation
rosbag replay simulation.
Info
Since this page explains Docker-specific information, it is recommended to see Source installation as well if you need detailed information.
Here are two ways to install Autoware by docker:
prebuilt image, this is a quick start, this way you can only run Autoware simulator and not develop Autoware, it is only suitable for beginnersdevel image, which supports developing and running Autoware using dockerdocker installation for quick start
"},{"location":"installation/autoware/docker-installation/#docker-installation-for-development","title":"Docker installation for development","text":"docker installation for development
"},{"location":"installation/autoware/docker-installation/#troubleshooting","title":"Troubleshooting","text":"Here are solutions for a few specific errors:
"},{"location":"installation/autoware/docker-installation/#cuda-error-forward-compatibility-was-attempted-on-non-supported-hw","title":"cuda error: forward compatibility was attempted on non supported hw","text":"When starting Docker with GPU support enabled for NVIDIA graphics, you may sometimes receive the following error:
docker: Error response from daemon: OCI runtime create failed: container_linux.go:349: starting container process caused \"process_linux.go:449: container init caused \\\"process_linux.go:432: running prestart hook 0 caused \\\\\\\"error running hook: exit status 1, stdout: , stderr: nvidia-container-cli: initialization error: cuda error: forward compatibility was attempted on non supported hw\\\\\\\\n\\\\\\\"\\\"\": unknown.\nERROR: Command return non-zero exit code (see above): 125\nThis usually indicates that a new NVIDIA graphics driver has been installed (usually via apt) but the system has not yet been restarted. A similar message may appear if the graphics driver is not available, for example because of resuming after suspend.
To fix this, restart your system after installing the new NVIDIA driver.
"},{"location":"installation/autoware/docker-installation/#docker-with-nvidia-gpu-fails-to-start-autoware-on-arm64-devices","title":"Docker with NVIDIA gpu fails to start Autoware on arm64 devices","text":"When starting Docker with GPU support enabled for NVIDIA graphics on arm64 devices, e.g. NVIDIA jetson AGX xavier, you may receive the following error:
nvidia@xavier:~$ rocker --nvidia --x11 --user --volume $HOME/autoware -- ghcr.io/autowarefoundation/autoware-universe:humble-latest-cuda-arm64\n...\n\nCollecting staticx==0.12.3\nDownloading https://files.pythonhosted.org/packages/92/ff/d9960ea1f9db48d6044a24ee0f3d78d07bcaddf96eb0c0e8806f941fb7d3/staticx-0.12.3.tar.gz (68kB)\nComplete output from command python setup.py egg_info:\nTraceback (most recent call last):\nFile \"\", line 1, in\nFile \"/tmp/pip-install-m_nm8mya/staticx/setup.py\", line 4, in\nfrom wheel.bdist_wheel import bdist_wheel\nModuleNotFoundError: No module named 'wheel'\n\nCommand \"python setup.py egg_info\" failed with error code 1 in /tmp/pip-install-m_nm8mya/staticx/\n...\nThis error exists in current version of rocker tool, which relates to the os_detection function of rocker.
To fix this error, temporary modification of rocker source code is required, which is not recommended.
At current stage, it is recommended to run docker without NVIDIA gpu enabled for arm64 devices:
rocker -e LIBGL_ALWAYS_SOFTWARE=1 --x11 --user --volume $HOME/autoware -- ghcr.io/autowarefoundation/autoware-universe:latest-cuda\nThis tutorial will be updated after official fix from rocker.
"},{"location":"installation/autoware/docker-installation/#tips","title":"Tips","text":""},{"location":"installation/autoware/docker-installation/#non-native-arm64-system","title":"Non-native arm64 System","text":"This section describes a process to run arm64 systems on amd64 systems using qemu-user-static.
Initially, your system is usually incompatible with arm64 systems. To check that:
$ docker run --rm -t arm64v8/ubuntu uname -m\nWARNING: The requested image's platform (linux/arm64/v8) does not match the detected host platform (linux/amd64) and no specific platform was requested\nstandard_init_linux.go:228: exec user process caused: exec format error\nInstalling qemu-user-static enables us to run arm64 images on amd64 systems.
$ sudo apt-get install qemu-user-static\n$ docker run --rm --privileged multiarch/qemu-user-static --reset -p yes\n$ docker run --rm -t arm64v8/ubuntu uname -m\nWARNING: The requested image's platform (linux/arm64/v8) does not match the detected host platform (linux/amd64) and no specific platform was requested\naarch64\nTo run Autoware's Docker images of arm64 architecture, add the suffix -arm64.
$ docker run --rm -it ghcr.io/autowarefoundation/autoware-universe:humble-latest-cuda-arm64\nWARNING: The requested image's platform (linux/arm64) does not match the detected host platform (linux/amd64) and no specific platform was requested\nroot@5b71391ad50f:/autoware#\nOS
ROS
For ROS 2 system dependencies, refer to REP-2000.
sudo apt-get -y update\nsudo apt-get -y install git\nNote: If you wish to use ROS 2 Galactic on Ubuntu 20.04, refer to installation instruction from galactic branch, but be aware that Galactic version of Autoware might not have latest features.
"},{"location":"installation/autoware/source-installation/#how-to-set-up-a-development-environment","title":"How to set up a development environment","text":"Clone autowarefoundation/autoware and move to the directory.
git clone https://github.com/autowarefoundation/autoware.git\ncd autoware\nIf you are installing Autoware for the first time, you can automatically install the dependencies by using the provided Ansible script.
./setup-dev-env.sh\nIf you encounter any build issues, please consult the Troubleshooting section for assistance.
Info
Before installing NVIDIA libraries, please ensure that you have reviewed and agreed to the licenses.
Note
The following items will be automatically installed. If the ansible script doesn't work or if you already have different versions of dependent libraries installed, please install the following items manually.
Create the src directory and clone repositories into it.
Autoware uses vcstool to construct workspaces.
cd autoware\nmkdir src\nvcs import src < autoware.repos\nInstall dependent ROS packages.
Autoware requires some ROS 2 packages in addition to the core components. The tool rosdep allows an automatic search and installation of such dependencies. You might need to run rosdep update before rosdep install.
source /opt/ros/humble/setup.bash\nrosdep install -y --from-paths src --ignore-src --rosdistro $ROS_DISTRO\nBuild the workspace.
Autoware uses colcon to build workspaces. For more advanced options, refer to the documentation.
colcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release\nIf there is any build issue, refer to Troubleshooting.
Update the .repos file.
cd autoware\ngit pull\nUpdate the repositories.
vcs import src < autoware.repos\nvcs pull src\nFor Git users:
vcs import is similar to git checkout.vcs pull is similar to git pull.For more information, refer to the official documentation.
Install dependent ROS packages.
source /opt/ros/humble/setup.bash\nrosdep install -y --from-paths src --ignore-src --rosdistro $ROS_DISTRO\nBuild the workspace.
colcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release\nWarning
Under Construction
"},{"location":"support/","title":"Support","text":""},{"location":"support/#support","title":"Support","text":"This page explains several support resources.
This page explains several documentation sites that are useful for Autoware and ROS development.
This page explains the support mechanisms we provide.
Warning
Before asking for help, search and read this documentation site carefully. Also, follow the discussion guidelines for discussions.
Choose appropriate resources depending on what kind of help you need and read the detailed description in the sections below.
Docs guide shows the list of useful documentation sites. Visit them and see if there is any information related to your problem.
Note that the documentation sites aren't always up-to-date and perfect. If you find out that some information is wrong, unclear, or missing in Autoware docs, feel free to submit a pull request following the contribution guidelines.
Warning
Since this documentation site is still under construction, there are some empty pages.
"},{"location":"support/support-guidelines/#github-discussions","title":"GitHub Discussions","text":"If you encounter a problem with Autoware, check existing issues and questions and search for similar issues first.
Issues
Note that Autoware has multiple repositories listed in autoware.repos. It is recommended to search across the repositories.
If no answer was found, create a new question thread here. If your question is not answered within a week, then @mention the maintainers to remind them.
Also, there are other discussion types such as feature requests or design discussions. Feel free to open or join such discussions.
If you don't know how to create a discussion, refer to GitHub Docs.
"},{"location":"support/support-guidelines/#github-issues","title":"GitHub Issues","text":"If you have a problem and you have confirmed it is a bug, find the appropriate repository and create a new issue there. If you can't determine the appropriate repository, ask the maintainers for help by creating a new discussion in the Q&A category.
Warning
Do not create issues for questions or unconfirmed bugs. If such issues are created, maintainers will transfer them to GitHub Discussions.
If you want to fix the bug by yourself, discuss the approach with maintainers and submit a pull request.
"},{"location":"support/support-guidelines/#discord","title":"Discord","text":"Autoware has a Discord server for casual communication between contributors.
The Autoware Discord server is a good place for the following activities:
Note that it is not the right place to get help for your issues.
"},{"location":"support/support-guidelines/#ros-discourse","title":"ROS Discourse","text":"If you want to widely discuss a topic with the general Autoware and ROS community or ask a question not related to Autoware's bugs, post to the Autoware category on ROS Discourse.
Warning
Do not post questions about bugs to ROS Discourse!
"},{"location":"support/troubleshooting/","title":"Troubleshooting","text":""},{"location":"support/troubleshooting/#troubleshooting","title":"Troubleshooting","text":""},{"location":"support/troubleshooting/#setup-issues","title":"Setup issues","text":""},{"location":"support/troubleshooting/#cuda-related-errors","title":"CUDA-related errors","text":"When installing CUDA, errors may occur because of version conflicts. To resolve these types of errors, try one of the following methods:
Unhold all CUDA-related libraries and rerun the setup script.
sudo apt-mark unhold \\\n\"cuda*\" \\\n\"libcudnn*\" \\\n\"libnvinfer*\" \\\n\"libnvonnxparsers*\" \\\n\"libnvparsers*\" \\\n\"tensorrt*\" \\\n\"nvidia*\"\n\n./setup-dev-env.sh\nUninstall all CUDA-related libraries and rerun the setup script.
sudo apt purge \\\n\"cuda*\" \\\n\"libcudnn*\" \\\n\"libnvinfer*\" \\\n\"libnvonnxparsers*\" \\\n\"libnvparsers*\" \\\n\"tensorrt*\" \\\n\"nvidia*\"\n\nsudo apt autoremove\n\n./setup-dev-env.sh\nWarning
Note that this may break your system and run carefully.
Run the setup script without installing CUDA-related libraries.
./setup-dev-env.sh --no-nvidia\nWarning
Note that some components in Autoware Universe require CUDA, and only the CUDA version in the env file is supported at this time. Autoware may work with other CUDA versions, but those versions are not supported and functionality is not guaranteed.
"},{"location":"support/troubleshooting/#build-issues","title":"Build issues","text":""},{"location":"support/troubleshooting/#insufficient-memory","title":"Insufficient memory","text":"Building Autoware requires a lot of memory, and your machine can freeze or crash if memory runs out during a build. To avoid this problem, 16-32GB of swap should be configured.
# Optional: Check the current swapfile\nfree -h\n\n# Remove the current swapfile\nsudo swapoff /swapfile\nsudo rm /swapfile\n\n# Create a new swapfile\nsudo fallocate -l 32G /swapfile\nsudo chmod 600 /swapfile\nsudo mkswap /swapfile\nsudo swapon /swapfile\n\n# Optional: Check if the change is reflected\nfree -h\nFor more detailed configuration steps, along with an explanation of swap, refer to Digital Ocean's \"How To Add Swap Space on Ubuntu 20.04\" tutorial
If there are too many CPU cores (more than 64) in your machine, it might requires larger memory. A workaround here is to limit the job number while building.
MAKEFLAGS=\"-j4\" colcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release\nYou can adjust -j4 to any number based on your system. For more details, see the manual page of GNU make.
By reducing the number of packages built in parallel, you can also reduce the amount of memory used. In the following example, the number of packages built in parallel is set to 1, and the number of jobs used by make is limited to 1.
MAKEFLAGS=\"-j1\" colcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release --parallel-workers 1\nNote
By lowering both the number of packages built in parallel and the number of jobs used by make, you can reduce the memory usage. However, this also means that the build process takes longer.
If you are working with the latest version of Autoware, issues can occur due to out-of-date software or old build files.
To resolve these types of problems, first try cleaning your build artifacts and rebuilding:
rm -rf build/ install/ log/\ncolcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release\nIf the error is not resolved, remove src/ and update your workspace according to installation type (Docker / source).
Warning
Before removing src/, confirm that there are no modifications in your local environment that you want to keep!
If errors still persist after trying the steps above, delete the entire workspace, clone the repository once again and restart the installation process.
rm -rf autoware/\ngit clone https://github.com/autowarefoundation/autoware.git\nIn principle, errors should not occur when using a fixed version. That said, possible causes include:
.bashrc file, environment variables, and library versions.In addition to the causes listed above, there are two common misunderstandings around the use of fixed versions.
You used a fixed version for autowarefoundation/autoware only. All of the repository versions in the .repos file must be specified in order to use a completely fixed version.
You didn't update the workspace after changing the branch of autowarefoundation/autoware. Changing the branch of autowarefoundation/autoware does not affect the files under src/. You have to run the vcs import command to update them.
During building the following issue can occurs
pkg_resources.extern.packaging.version.InvalidVersion: Invalid version: '0.23ubuntu1'\nThe error is due to the fact that for versions between 66.0.0 and 67.5.0 setuptools enforces the python packages to be PEP-440 conformant. Since version 67.5.1 setuptools has a fallback that makes it possible to work with old packages again.
The solution is to update setuptools to the newest version with the following command
pip install --upgrade setuptools\nIf any errors occur when running Autoware with Docker or rocker, first confirm that your Docker installation is working correctly by running the following commands:
docker run --rm -it hello-world\ndocker run --rm -it ubuntu:latest\nNext, confirm that you are able to access the base Autoware image that is stored on the GitHub Packages website
docker run --rm -it ghcr.io/autowarefoundation/autoware-universe:latest\nSymptoms:
If you have any of these symptoms, please the Performance Troubleshooting page.
"},{"location":"support/troubleshooting/#map-does-not-display-when-running-the-planning-simulator","title":"Map does not display when running the Planning Simulator","text":"When running the Planning Simulator, the most common reason for the map not being displayed in RViz is because the map path has not been specified correctly in the launch command. You can confirm if this is the case by searching for Could not find lanelet map under {path-to-map-dir}/lanelet2_map.osm errors in the log.
Another possible reason is that map loading is taking a long time due to poor DDS performance. For this, please visit the Performance Troubleshooting page.
"},{"location":"support/troubleshooting/performance-troubleshooting/","title":"Performance Troubleshooting","text":""},{"location":"support/troubleshooting/performance-troubleshooting/#performance-troubleshooting","title":"Performance Troubleshooting","text":"Overall symptoms:
Make sure that the multicast is enabled for your interface.
For example when you run following:
source /opt/ros/humble/setup.bash\nros2 run demo_nodes_cpp talker\nIf you get the error message selected interface \"{your-interface-name}\" is not multicast-capable: disabling multicast, this should be fixed.
Run the following command to allow multicast:
sudo ip link set multicast on {your-interface-name}\nThis way DDS will function as intended and multiple subscribers can receive data from a single publisher without any significant degradation in performance.
This is a temporary solution. And will be reverted once the computer restarts.
To make it permanent either,
OR put following lines to the ~/.bashrc file:
if [ ! -e /tmp/multicast_is_set ]; then\nsudo ip link set lo multicast on\ntouch /tmp/multicast_is_set\nfi\nCheck the ~/.bash_history file to see if there are any colcon build directives without -DCMAKE_BUILD_TYPE=Release or -DCMAKE_BUILD_TYPE=RelWithDebInfo flags at all.
Even if a build starts with these flags but same workspace gets compiled without these flags, it will still be a slow build in the end.
In addition, the nodes will run slow in general, especially the pointcloud_preprocessor nodes.
Example issue: issue2597
"},{"location":"support/troubleshooting/performance-troubleshooting/#solution_1","title":"Solution","text":"build, install and optionally log folders in the main autoware folder.Compile the Autoware with either Release or RelWithDebInfo tags:
colcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release\n# Or build with debug flags too (comparable performance but you can debug too)\ncolcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=RelWithDebInfo\nRun following to check the middleware used:
echo $RMW_IMPLEMENTATION\nThe return line should be rmw_cyclonedds_cpp. If not, apply the solution.
If you are using a different DDS middleware, we might not have official support for it just yet.
"},{"location":"support/troubleshooting/performance-troubleshooting/#solution_2","title":"Solution","text":"Add export RMW_IMPLEMENTATION=rmw_cyclonedds_cpp as a separate line in you ~/.bashrc file.
Run following to check the configuration .xml file of the CycloneDDS:
echo $CYCLONEDDS_URI\nThe return line should be a valid path pointing to an .xml file with CycloneDDS configuration.
Also check if the file is configured correctly:
cat !{echo $CYCLONEDDS_URI}\nThis should print the .xml file on the terminal.
Follow DDS settings:Tuning DDS documentation and make sure:
export CYCLONEDDS_URI=/absolute_path_to_your/cyclonedds_config.xml as a line on your ~/.bashrc file.cyclonedds_config.xml with the configuration provided in the documentation.sysctl net.core.rmem_max, it should return at least net.core.rmem_max = 2147483647.sysctl net.ipv4.ipfrag_time, it should return around: net.ipv4.ipfrag_time = 3sysctl net.ipv4.ipfrag_high_thresh, it should return at around: net.ipv4.ipfrag_high_thresh = 134217728More info on these values: Cross-vendor tuning
"},{"location":"support/troubleshooting/performance-troubleshooting/#solution_4","title":"Solution","text":"Either:
Create the following file: sudo touch /etc/sysctl.d/10-cyclone-max.conf (recommended)
Edit the file to contain (sudo gedit /etc/sysctl.d/10-cyclone-max.conf):
net.core.rmem_max=2147483647\nnet.ipv4.ipfrag_time=3\nnet.ipv4.ipfrag_high_thresh=134217728 # (128 MB)\nEither restart the computer or run following to enable the changes:
sudo sysctl -w net.core.rmem_max=2147483647\nsudo sysctl -w net.ipv4.ipfrag_time=3\nsudo sysctl -w net.ipv4.ipfrag_high_thresh=134217728\nOR put following lines to the ~/.bashrc file:
if [ ! -e /tmp/kernel_network_conf_is_set ]; then\nsudo sysctl -w net.core.rmem_max=2147483647\nsudo sysctl -w net.ipv4.ipfrag_time=3\nsudo sysctl -w net.ipv4.ipfrag_high_thresh=134217728 # (128 MB)\nfi\nRun following to check it:
echo $ROS_LOCALHOST_ONLY\nThe return line should be 1. If not, apply the solution.
export $ROS_LOCALHOST_ONLY=1 as a separate line in you ~/.bashrc file.loopback network interface (i.e., localhost) for communication, rather than using the network interface card (NIC) for Ethernet or Wi-Fi. This can reduce network traffic and potential conflicts with other devices on the network, resulting in better performance and stability.Simulations provide a way of verifying Autoware's functionality before field testing with an actual vehicle. There are three main types of simulation that can be run ad hoc or via a scenario runner.
"},{"location":"tutorials/#simulation-methods","title":"Simulation methods","text":""},{"location":"tutorials/#ad-hoc-simulation","title":"Ad hoc simulation","text":"Ad hoc simulation is a flexible method for running basic simulations on your local machine, and is the recommended method for anyone new to Autoware.
"},{"location":"tutorials/#scenario-simulation","title":"Scenario simulation","text":"Scenario simulation uses a scenario runner to run more complex simulations based on predefined scenarios. It is often run automatically for continuous integration purposes, but can also be run on a local machine.
"},{"location":"tutorials/#simulation-types","title":"Simulation types","text":""},{"location":"tutorials/#planning-simulation","title":"Planning simulation","text":"Planning simulation uses simple dummy data to test the Planning and Control components - specifically path generation, path following and obstacle avoidance. It verifies that a vehicle can reach a goal destination while avoiding pedestrians and surrounding cars, and is another method for verifying the validity of Lanelet2 maps. It also allows for testing of traffic light handling.
"},{"location":"tutorials/#how-does-planning-simulation-work","title":"How does planning simulation work?","text":"Rosbag replay simulation uses prerecorded rosbag data to test the following aspects of the Localization and Perception components:
By repeatedly playing back the data, this simulation type can also be used for endurance testing.
"},{"location":"tutorials/#digital-twin-simulation","title":"Digital twin simulation","text":"Digital twin simulation is a simulation type that is able to produce realistic data and simulate almost the entire system. It is also commonly referred to as end-to-end simulation.
"},{"location":"tutorials/ad-hoc-simulation/","title":"Ad hoc simulation","text":""},{"location":"tutorials/ad-hoc-simulation/#ad-hoc-simulation","title":"Ad hoc simulation","text":"Warning
Under Construction
"},{"location":"tutorials/ad-hoc-simulation/planning-simulation/","title":"Planning simulation","text":""},{"location":"tutorials/ad-hoc-simulation/planning-simulation/#planning-simulation","title":"Planning simulation","text":""},{"location":"tutorials/ad-hoc-simulation/planning-simulation/#preparation","title":"Preparation","text":"Download and unpack a sample map.
gdown -O ~/autoware_map/ 'https://docs.google.com/uc?export=download&id=1499_nsbUbIeturZaDj7jhUownh5fvXHd'\nunzip -d ~/autoware_map ~/autoware_map/sample-map-planning.zip\nNote
Sample map: Copyright 2020 TIER IV, Inc.
"},{"location":"tutorials/ad-hoc-simulation/planning-simulation/#basic-simulations","title":"Basic simulations","text":""},{"location":"tutorials/ad-hoc-simulation/planning-simulation/#lane-driving-scenario","title":"Lane driving scenario","text":""},{"location":"tutorials/ad-hoc-simulation/planning-simulation/#1-launch-autoware","title":"1. Launch Autoware","text":"source ~/autoware/install/setup.bash\nros2 launch autoware_launch planning_simulator.launch.xml map_path:=$HOME/autoware_map/sample-map-planning vehicle_model:=sample_vehicle sensor_model:=sample_sensor_kit\nWarning
Note that you cannot use ~ instead of $HOME here.
If ~ is used, the map will fail to load.
a) Click the 2D Pose estimate button in the toolbar, or hit the P key.
b) In the 3D View pane, click and hold the left-mouse button, and then drag to set the direction for the initial pose. An image representing the vehicle should now be displayed.
Warning
Remember to set the initial pose of the car in the same direction as the lane.
To confirm the direction of the lane, check the arrowheads displayed on the map.
"},{"location":"tutorials/ad-hoc-simulation/planning-simulation/#3-set-a-goal-pose-for-the-ego-vehicle","title":"3. Set a goal pose for the ego vehicle","text":"a) Click the 2D Goal Pose button in the toolbar, or hit the G key.
b) In the 3D View pane, click and hold the left-mouse button, and then drag to set the direction for the goal pose. If done correctly, you will see a planned path from initial pose to goal pose.
"},{"location":"tutorials/ad-hoc-simulation/planning-simulation/#4-start-the-ego-vehicle","title":"4. Start the ego vehicle","text":"Now you can start the ego vehicle driving by clicking the AUTO button on OperationMode in AutowareStatePanel. Alteratively, you can manually start the vehicle by running the following command:
source ~/autoware/install/setup.bash\nros2 service call /api/operation_mode/change_to_autonomous autoware_adapi_v1_msgs/srv/ChangeOperationMode {}\nAfter that, you can see AUTONOMOUS sign on OperationMode and AUTO button is grayed out.
Set an initial pose and a goal pose, and engage the ego vehicle.
When the vehicle approaches the goal, it will switch from lane driving mode to parking mode.
After that, the vehicle will reverse into the destination parking spot.
2D Dummy Car or 2D Dummy Pedestrian button in the toolbar.Set the velocity of the object in Tool Properties -> 2D Dummy Car/Pedestrian panel.
!!! note
Changes to the velocity parameter will only affect objects placed after the parameter is changed.
Delete any dummy objects placed in the view by clicking the Delete All Objects button in the toolbar.
Click the Interactive button in the toolbar to make the dummy object interactive.
For adding an interactive dummy object, press SHIFT and click the right click.
ALT and click the right click.For moving an interactive dummy object, hold the right click drag and drop the object.
By default, traffic lights on the map are all treated as if they are set to green. As a result, when a path is created that passed through an intersection with a traffic light, the ego vehicle will drive through the intersection without stopping.
The following steps explain how to set and reset traffic lights in order to test how the Planning component will respond.
"},{"location":"tutorials/ad-hoc-simulation/planning-simulation/#set-traffic-light","title":"Set traffic light","text":"Go to Panels -> Add new panel, select TrafficLightPublishPanel, and then press OK.
In TrafficLightPublishPanel, set the ID and color of the traffic light.
Click the SET button.
Finally, click the PUBLISH button to send the traffic light status to the simulator. Any planned path that goes past the selected traffic light will then change accordingly.
By default, Rviz should display the ID of each traffic light on the map. You can have a closer look at the IDs by zooming in the region or by changing the View type.
In case the IDs are not displayed, try the following troubleshooting steps:
a) In the Displays panel, find the traffic_light_id topic by toggling the triangle icons next to Map > Lanelet2VectorMap > Namespaces.
b) Check the traffic_light_id checkbox.
c) Reload the topic by clicking the Map checkbox twice.
You can update the color of the traffic light by selecting the next color (in the image it is GREEN) and clicking SET button. In the image the traffic light in front of the ego vehicle changed from RED to GREEN and the vehicle restarted.
To remove a traffic light from TrafficLightPublishPanel, click the RESET button.
Reference video tutorials
"},{"location":"tutorials/ad-hoc-simulation/rosbag-replay-simulation/","title":"Rosbag replay simulation","text":""},{"location":"tutorials/ad-hoc-simulation/rosbag-replay-simulation/#rosbag-replay-simulation","title":"Rosbag replay simulation","text":""},{"location":"tutorials/ad-hoc-simulation/rosbag-replay-simulation/#steps","title":"Steps","text":"Download and unpack a sample map.
gdown -O ~/autoware_map/ 'https://docs.google.com/uc?export=download&id=1A-8BvYRX3DhSzkAnOcGWFw5T30xTlwZI'\nunzip -d ~/autoware_map/ ~/autoware_map/sample-map-rosbag.zip\nDownload the sample rosbag files.
gdown -O ~/autoware_map/ 'https://docs.google.com/uc?export=download&id=1VnwJx9tI3kI_cTLzP61ktuAJ1ChgygpG'\nunzip -d ~/autoware_map/ ~/autoware_map/sample-rosbag.zip\nLaunch Autoware.
source ~/autoware/install/setup.bash\nros2 launch autoware_launch logging_simulator.launch.xml map_path:=$HOME/autoware_map/sample-map-rosbag vehicle_model:=sample_vehicle sensor_model:=sample_sensor_kit\nNote that you cannot use ~ instead of $HOME here.
Play the sample rosbag file.
source ~/autoware/install/setup.bash\nros2 bag play ~/autoware_map/sample-rosbag/sample.db3 -r 0.2 -s sqlite3\nTo focus the view on the ego vehicle, change the Target Frame in the RViz Views panel from viewer to base_link.
To switch the view to Third Person Follower etc, change the Type in the RViz Views panel.
Reference video tutorials
"},{"location":"tutorials/ad-hoc-simulation/digital-twin-simulation/MORAI_Sim-tutorial/","title":"MORAI Sim: Drive","text":""},{"location":"tutorials/ad-hoc-simulation/digital-twin-simulation/MORAI_Sim-tutorial/#morai-sim-drive","title":"MORAI Sim: Drive","text":"Note
Any kind of for-profit activity with the trial version of the MORAI SIM:Drive is strictly prohibited.
"},{"location":"tutorials/ad-hoc-simulation/digital-twin-simulation/MORAI_Sim-tutorial/#hardware-requirements","title":"Hardware requirements","text":"Minimum PC Specs OS Windows 10, Ubuntu 20.04, Ubuntu 18.04, Ubuntu 16.04 CPU Intel i5-9600KF or AMD Ryzen 5 3500X RAM DDR4 16GB GPU RTX2060 Super Required PC Specs OS Windows 10, Ubuntu 20.04, Ubuntu 18.04, Ubuntu 16.04 CPU Intel i9-9900K or AMD Ryzen 7 3700X (or higher) RAM DDR4 64GB (or higher) GPU RTX2080Ti or higher"},{"location":"tutorials/ad-hoc-simulation/digital-twin-simulation/MORAI_Sim-tutorial/#application-and-download","title":"Application and Download","text":"Only for AWF developers, trial license for 3 months can be issued. Download the application form and send to Hyeongseok Jeon
After the trial license is issued, you can login to MORAI Sim:Drive via Launchers (Windows/Ubuntu)
CAUTION: Do not use the Launchers in the following manual
"},{"location":"tutorials/ad-hoc-simulation/digital-twin-simulation/MORAI_Sim-tutorial/#technical-documents","title":"Technical Documents","text":"as Oct. 2022, our simulation version is ver.22.R3 but the english manual is under construction.
Be aware that the following manuals are for ver.22.R2
Hyeongseok Jeon will give full technical support
AWSIM is a simulator for Autoware development and testing. To get started, please follow the official instruction provided by TIER IV.
"},{"location":"tutorials/scenario-simulation/","title":"Scenario simulation","text":""},{"location":"tutorials/scenario-simulation/#scenario-simulation","title":"Scenario simulation","text":"Warning
Under Construction
"},{"location":"tutorials/scenario-simulation/planning-simulation/installation/","title":"Installation","text":""},{"location":"tutorials/scenario-simulation/planning-simulation/installation/#installation","title":"Installation","text":"This document contains step-by-step instruction on how to build AWF Autoware Core/Universe with scenario_simulator_v2.
Navigate to the Autoware workspace:
cd autoware\nImport Simulator dependencies:
vcs import src < simulator.repos\nInstall dependent ROS packages:
source /opt/ros/humble/setup.bash\nrosdep install -y --from-paths src --ignore-src --rosdistro $ROS_DISTRO\nBuild the workspace:
colcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release\nNote
Running the Scenario Simulator requires some additional steps on top of building and installing Autoware, so make sure that Scenario Simulator installation has been completed first before proceeding.
"},{"location":"tutorials/scenario-simulation/planning-simulation/random-test-simulation/#running-steps","title":"Running steps","text":"Move to the workspace directory where Autoware and the Scenario Simulator have been built.
Source the workspace setup script:
source install/setup.bash\nRun the simulation:
ros2 launch random_test_runner random_test.launch.py \\\narchitecture_type:=awf/universe \\\nsensor_model:=sample_sensor_kit \\\nvehicle_model:=sample_vehicle\nFor more information about supported parameters, refer to the random_test_runner documentation.
"},{"location":"tutorials/scenario-simulation/planning-simulation/scenario-test-simulation/","title":"Scenario test simulation","text":""},{"location":"tutorials/scenario-simulation/planning-simulation/scenario-test-simulation/#scenario-test-simulation","title":"Scenario test simulation","text":"Note
Running the Scenario Simulator requires some additional steps on top of building and installing Autoware, so make sure that Scenario Simulator installation has been completed first before proceeding.
"},{"location":"tutorials/scenario-simulation/planning-simulation/scenario-test-simulation/#running-steps","title":"Running steps","text":"Move to the workspace directory where Autoware and the Scenario Simulator have been built.
Source the workspace setup script:
source install/setup.bash\nRun the simulation:
ros2 launch scenario_test_runner scenario_test_runner.launch.py \\\narchitecture_type:=awf/universe \\\nrecord:=false \\\nscenario:='$(find-pkg-share scenario_test_runner)/scenario/sample.yaml' \\\nsensor_model:=sample_sensor_kit \\\nvehicle_model:=sample_vehicle\nReference video tutorials
"},{"location":"tutorials/scenario-simulation/rosbag-replay-simulation/driving-log-replayer/","title":"Driving Log Replayer","text":""},{"location":"tutorials/scenario-simulation/rosbag-replay-simulation/driving-log-replayer/#driving-log-replayer","title":"Driving Log Replayer","text":"Driving Log Replayer is an evaluation tool for Autoware. To get started, follow the official instruction provided by TIER IV.
"}]} \ No newline at end of file diff --git a/pr-385/sitemap.xml b/pr-385/sitemap.xml index d6252a68b9a..0b88c637e57 100644 --- a/pr-385/sitemap.xml +++ b/pr-385/sitemap.xml @@ -2,877 +2,877 @@