"},{"location":"faq.html#_1","title":"\u74b0\u5883\u69cb\u7bc9","text":""},{"location":"faq.html#awsim-and-autoware","title":"AWSIM and Autoware\u9593\u306e\u901a\u4fe1\u304c\u5b89\u5b9a\u3057\u307e\u305b\u3093\u3002","text":"
local \u3067\u30c6\u30b9\u30c8\u3059\u308b\u969b\u3001\u3059\u3079\u3066\u306e terminal \u3067ROS_LOCALHOST_ONLY=1\u306b\u8a2d\u5b9a\u3059\u308b\u3068\u901a\u4fe1\u901f\u5ea6\u304c\u5411\u4e0a\u3057\u307e\u3059\u3002 .bashrc \u306b\u4ee5\u4e0b\u306e\u884c\u3092\u8ffd\u52a0\u3057\u3066\u304f\u3060\u3055\u3044\u3002
export ROS_LOCALHOST_ONLY=1\nexport RMW_IMPLEMENTATION=rmw_cyclonedds_cpp\n\nif [ ! -e /tmp/cycloneDDS_configured ]; then\n sudo sysctl -w net.core.rmem_max=2147483647\n sudo ip link set lo multicast on\n touch /tmp/cycloneDDS_configured\n
OS \u306e\u8d77\u52d5\u5f8c\u3001\u30bf\u30fc\u30df\u30ca\u30eb\u306e\u8d77\u52d5\u6642\u306b\u30d1\u30b9\u30ef\u30fc\u30c9\u304c\u8981\u6c42\u3055\u308c\u3001\u521d\u56de\u306b\u306f sudo ip link set lo multicast on \u304c\u5fc5\u8981\u3067\u3059\u3002
"},{"location":"faq.html#windowsawsimubuntuautoware-ros2-topic-list","title":"Windows\u306eAWSIM\u3068Ubuntu\u306eAutoware\u3092\u4f7f\u7528\u3057\u3066\u304a\u308a\u3001$ ros2 topic list \u304c\u8868\u793a\u3055\u308c\u307e\u305b\u3093\u3002","text":"
Windows Firewall\u3067\u306e\u901a\u4fe1\u3092\u8a31\u53ef\u3057\u3066\u304f\u3060\u3055\u3044\u3002 \u307e\u305f\u3001ros2 daemon stop\u3068ros2 daemon start\u3092\u5b9f\u884c\u3057\u3066\u3001\u4e0d\u8981\u306a\u30d7\u30ed\u30bb\u30b9\u304c\u6b8b\u3063\u3066\u3044\u306a\u3044\u304b\u78ba\u8a8d\u3057\u3001\u518d\u8d77\u52d5\u3092\u304a\u9858\u3044\u3057\u307e\u3059\u3002
"},{"location":"faq.html#warning-unable-to-detect-os-for-base-image-aichallenge-2024-dev-maybe-the-base-image-does-not-exist","title":"WARNING unable to detect os for base image 'aichallenge-2024-dev', maybe the base image does not exist\u304c\u51fa\u307e\u3059\u3002","text":"
newgrp docker\u304bsudo service docker restart\u3067docker\u306e\u518d\u8d77\u52d5\u307e\u305f\u306fUbuntu\u306e\u518d\u8d77\u52d5\u3092\u304a\u9858\u3044\u3057\u307e\u3059\u3002
"},{"location":"faq.html#_2","title":"\u64cd\u4f5c","text":""},{"location":"faq.html#ros","title":"ROS","text":""},{"location":"faq.html#python-no-module-named-error","title":"python\u3067\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u4f5c\u6210\u3059\u308b\u3068\u5b9f\u884c\u6642 no module named * \u306eerror\u304c\u8d77\u304d\u307e\u3059\u3002","text":"
topic\u306e\u578b\u3092\u8abf\u3079\u308b\u969b\u306fros2 topic info -v fuga_topic\u3082\u3057\u304f\u306fnode\u304c\u7279\u5b9a\u3067\u304d\u308c\u3070\u3001ros2 node info hoge-node\u3067\u8abf\u3079\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002 \u305d\u306e\u4ed6\u306b\u3082ROS\u306b\u95a2\u3059\u308b\u60c5\u5831\u3092\u8abf\u3079\u305f\u3044\u5834\u5408\u306f\u300cROS2\u3000\u30b3\u30de\u30f3\u30c9\u300d\u3067\u3001\u30cd\u30c3\u30c8\u691c\u7d22\u3059\u308b\u3068\u826f\u3044\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u3002
center point\u306f\u8eca\u4e21\u3068\u30c8\u30e9\u30c3\u30af\u3068\u6b69\u884c\u8005\u3092\u691c\u77e5\u3057\u3066\u304f\u308c\u307e\u3059\u304c\u3001\u30c0\u30f3\u30dc\u30fc\u30eb\u306a\u3069\u30bf\u30b0\u4ed8\u3051\u3055\u308c\u3066\u3044\u306a\u3044\u3082\u306e\u306f\u691c\u77e5\u3067\u304d\u307e\u305b\u3093\u3002 \u305f\u3060\u3001\u73fe\u72b6\u306eautoware\u3068\u3057\u3066\u306fplanning\u304cobject\u3092\u53d7\u3051\u53d6\u3089\u306a\u3044\u3068\u52d5\u304b\u306a\u3044\u3088\u3046\u306b\u306a\u3063\u3066\u304a\u308a\u3001object\u3092\u53d7\u3051\u53d6\u308b\u6bb5\u968e\u3067center point\u3092\u4f7f\u3046\u30c7\u30d5\u30a9\u30eb\u30c8\u306e\u69cb\u6210\u306b\u3057\u3066\u3044\u308b\u3068\u3001\u4ee5\u4e0b\u306e2\u3064\u306e\u539f\u56e0\u306b\u3088\u308a\u4e0d\u5177\u5408\u304c\u8d77\u3053\u308a\u307e\u3059\u3002
center point\u304c\u6b7b\u3093\u3060\u3068\u304d\u306bplanning\u304c\u7d4c\u8def\u3092\u751f\u6210\u3067\u304d\u306a\u304f\u306a\u308b
data association\u3067clustering\u306b\u3088\u308b\u969c\u5bb3\u7269\u691c\u77e5\u7d50\u679c\u304c\u6d88\u3055\u308c\u308b
"},{"location":"index.html","title":"Japan Automotive AI Challenge 2024","text":""},{"location":"index.html#_1","title":"\u30b3\u30f3\u30bb\u30d7\u30c8","text":"
State lattice planner\u3068\u306f\u3001\u8eca\u4e21\u306e\u73fe\u5728\u306e\u72b6\u614b\u3068\u76ee\u6a19\u72b6\u614b\u306e\u9593\u306b\u4e00\u9023\u306e\u8ecc\u9053\u5019\u88dc\u3092\u751f\u6210\u3057\u3001\u305d\u308c\u305e\u308c\u306e\u8ecc\u9053\u3092\u8a55\u4fa1\u3057\u3066\u6700\u9069\u306a\u7d4c\u8def\u3092\u9078\u629e\u3059\u308b\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3067\u3059\u3002 \u4ee5\u4e0b\u306e\u753b\u50cf\u306b\u8ecc\u9053\u3092\u751f\u6210\u3059\u308b\u30d5\u30ed\u30fc\u3092\u793a\u3057\u307e\u3059\u3002
state lattice planner\u306e\u30d5\u30ed\u30fc"},{"location":"course/avoidance.html#1","title":"1. \u76ee\u6a19\u72b6\u614b\u3092\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0","text":"
pure pursuit\u306f\u3001\u8eca\u4e21\u306e\u73fe\u5728\u4f4d\u7f6e\u3068\u76ee\u6a19\u7d4c\u8def\u4e0a\u306e\u8ffd\u5f93\u70b9\uff08\u30eb\u30c3\u30af\u30a2\u30d8\u30c3\u30c9\u30dd\u30a4\u30f3\u30c8\uff09\u3068\u306e\u8ddd\u96e2\u3068\u65b9\u5411\u3092\u57fa\u306b\u30eb\u30c3\u30af\u30a2\u30d8\u30c3\u30c9\u30dd\u30a4\u30f3\u30c8\u306b\u5230\u9054\u3059\u308b\u305f\u3081\u306e\u66f2\u7387\u3092\u8a08\u7b97\u3059\u308b\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3067\u3059\u3002\u4ee5\u4e0b\u306bpure pursuit\u306e\u57fa\u672c\u7684\u306a\u52d5\u4f5c\u3092\u8aac\u660e\u3057\u307e\u3059\u3002
pure pursuit\u306e\u5229\u70b9\u306f\u3001\u305d\u306e\u30b7\u30f3\u30d7\u30eb\u3055\u3068\u5b9f\u88c5\u306e\u5bb9\u6613\u3055\u306b\u3042\u308a\u307e\u3059\u3002\u3057\u304b\u3057\u3001\u9ad8\u901f\u8d70\u884c\u3084\u6025\u30ab\u30fc\u30d6\u306e\u591a\u3044\u7d4c\u8def\u3067\u306f\u3001\u5225\u306e\u5236\u5fa1\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3068\u306e\u7d44\u307f\u5408\u308f\u305b\u304c\u5fc5\u8981\u306b\u306a\u308b\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002
Best Lap Time : \u4e88\u9078\u306fSIM\u3067\u8a08\u6e2c\u3001\u6c7a\u52dd\u306fTOM\u2019S\u306e\u30b7\u30b9\u30c6\u30e0\u3092\u4f7f\u7528
Best Comfortable Ride\uff1a\u4e88\u9078\u306fSIM\u3067\u8a08\u6e2c\u3001\u6c7a\u52dd\u306f\u6c34\u3092\u30b0\u30e9\u30b9\u306b\u5165\u308c\u3066\u8a08\u91cf\u3059\u308b\u3053\u3068\u3067\u5bfe\u5fdc
Hello from Docker!\u3068\u8868\u793a\u3055\u308c\u308c\u3070\u6b63\u5e38\u306b\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u51fa\u6765\u3066\u3044\u307e\u3059\u3002
#!/bin/bash\n\n# shellcheck disable=SC1091\nsource /aichallenge/workspace/install/setup.bash\nsudo ip link set multicast on lo\n/aichallenge/simulator/AWSIM_GPU_**/AWSIM.x86_64\n
\u9805\u76ee \u5024 \u8eca\u4e21\u91cd\u91cf 160 kg \u5168\u9577 200 cm \u5168\u5e45 145 cm \u30db\u30a4\u30fc\u30eb\u30d9\u30fc\u30b9 108.7 cm \u524d\u8f2a\u30bf\u30a4\u30e4\u76f4\u5f84 24 cm \u524d\u8f2a\u30bf\u30a4\u30e4\u5e45 13 cm \u524d\u8f2a\u30db\u30a4\u30fc\u30eb\u30c8\u30ec\u30c3\u30c9 93 cm \u5f8c\u8f2a\u30bf\u30a4\u30e4\u76f4\u5f84 24 cm \u5f8c\u8f2a\u30bf\u30a4\u30e4\u5e45 18 cm \u5f8c\u8f2a\u30db\u30a4\u30fc\u30eb\u30c8\u30ec\u30c3\u30c9 112 cm \u6700\u5927\u30b9\u30c6\u30a2\u30ea\u30f3\u30b0\u8ee2\u8235\u89d2 80 \u00b0 \u99c6\u52d5\u6642\u6700\u5927\u52a0\u901f\u5ea6 3.2 m/s^2"},{"location":"specifications/simulator.html#vehicle","title":"Vehicle\u30b3\u30f3\u30dd\u30fc\u30cd\u30f3\u30c8","text":"
\u9805\u76ee \u5024 Mass 160 Drag 0 Angular Drag 0"},{"location":"specifications/simulator.html#com","title":"CoM\u4f4d\u7f6e","text":"
CoM(Center of Mass)\u306f\u3001\u8eca\u4e21Rigidbody\u306e\u8cea\u91cf\u4e2d\u5fc3\u3067\u3059\u3002CoM\u4f4d\u7f6e\u306f\u3001\u8eca\u4e21\u306e\u4e2d\u5fc3\u304b\u3064\u8eca\u8f2a\u8ef8\u306e\u9ad8\u3055\u306b\u8a2d\u5b9a\u3055\u308c\u3066\u3044\u307e\u3059\u3002
\u9805\u76ee \u5024 x 0.0 m y 0.0 m z 0.0 m roll 0.0 rad pitch 0.0 rad yaw 0.0 rad"},{"location":"specifications/simulator.html#imu","title":"IMU","text":"
\u9805\u76ee \u5024 x 0.0 m y 0.0 m z 0.0 m roll 0.0 rad pitch 0.0 rad yaw 0.0 rad"},{"location":"en/community.html#autonomous-driving-ai-challenge-advent-calendar-2024","title":"Autonomous Driving AI Challenge Advent Calendar 2024","text":"
"},{"location":"en/community.html#articles-on-autonomous-driving-ai-challenge-efforts","title":"Articles on Autonomous Driving AI Challenge Efforts","text":"
Many issues can be resolved using ChatGPT or Google search. For questions that cannot be resolved, please include and attach excerpts of error logs.
"},{"location":"en/faq.html#environment-setup","title":"Environment Setup","text":""},{"location":"en/faq.html#the-communication-between-awsim-and-autoware-is-unstable","title":"The communication between AWSIM and Autoware is unstable.","text":"
When testing locally, setting ROS_LOCALHOST_ONLY=1 in all terminals can improve communication speed. Add the following lines to your .bashrc.
export ROS_LOCALHOST_ONLY=1\nexport RMW_IMPLEMENTATION=rmw_cyclonedds_cpp\n\nif [ ! -e /tmp/cycloneDDS_configured ]; then\n sudo sysctl -w net.core.rmem_max=2147483647\n sudo ip link set lo multicast on\n touch /tmp/cycloneDDS_configured\nfi\n
For a dual-PC setup (Windows+Linux or Linux+Linux), set ROS_LOCALHOST_ONLY=0.
Note:
After OS startup, you will need to enter the password when starting the terminal and execute sudo ip link set lo multicast on for the first time.
Always track changes by using commands like echo $ROS_LOCALHOST_ONLY to avoid forgetting modifications in .bashrc.
Mixed use of ROS_LOCALHOST_ONLY=1 and ROS_LOCALHOST_ONLY=0 will prevent container communication.
Ensure that ROS_LOCALHOST_ONLY is not hard-coded in the executable.
"},{"location":"en/faq.html#ros2-topic-list-does-not-display","title":"ros2 topic list does not display.","text":"
Ensure that the ROS_DOMAIN_ID matches on your machine (this is not an issue if you haven't set ROS_DOMAIN_ID). Also, ensure ROS2 is sourced correctly.
"},{"location":"en/faq.html#using-awsim-on-windows-and-autoware-on-ubuntu-ros2-topic-list-does-not-display","title":"Using AWSIM on Windows and Autoware on Ubuntu, ros2 topic list does not display.","text":"
Allow communication through the Windows Firewall. Also, execute ros2 daemon stop and ros2 daemon start to ensure no unnecessary processes are running, then restart.
"},{"location":"en/faq.html#rocker-does-not-start","title":"Rocker does not start.","text":"
First, verify that Rocker is installed. If it is installed but does not start, check the permissions. It has been reported that differing account types and permissions when building and running the image can cause issues.
"},{"location":"en/faq.html#awsim-terminates-with-a-core-dump","title":"AWSIM terminates with a core dump.","text":"
If AWSIM terminates with a core dump immediately after startup, your GPU may be out of memory. Check the GPU memory usage with nvidia-smi to ensure it is not at its limit. A GPU with at least 11GB of memory is recommended.
"},{"location":"en/faq.html#i-only-have-a-windows-pc-with-a-gpu","title":"I only have a Windows PC with a GPU.","text":"
The official support is for the configuration listed on the HP website, so detailed guidance cannot be provided, but generally, the following methods are possible:
The key is to \"prepare an environment to run Autoware,\" which may involve issues related to performance, package availability, and host-container communication settings. Possible solutions include:
Setting up Ubuntu in a dual-boot configuration.
Using a VM on Windows to run Ubuntu (Hyper-V, VirtualBox, VMware, etc.).
Setting up Ubuntu on WSL2.
Setting up a Docker environment on Windows and running the Autoware image directly.
Building the environment in the cloud (some past participants used AWS).
"},{"location":"en/faq.html#awsim-appears-but-rviz-shows-a-black-screen-when-set-up-on-aws","title":"AWSIM appears but Rviz shows a black screen when set up on AWS.","text":"
There have been cases where running sudo apt upgrade resolved the issue. Additionally, there is a similar question in a past issue that might be helpful.
"},{"location":"en/faq.html#docker_runsh-line-35-rocker-command-not-found-appears","title":"docker_run.sh: line 35: rocker: command not found appears.","text":"
Please install Rocker as described here.
"},{"location":"en/faq.html#warning-unable-to-detect-os-for-base-image-aichallenge-2024-dev-maybe-the-base-image-does-not-exist-appears","title":"WARNING unable to detect os for base image 'aichallenge-2024-dev', maybe the base image does not exist appears.","text":"
Please build the Docker image.
"},{"location":"en/faq.html#unable-to-pull-docker","title":"Unable to pull Docker.","text":"
Please restart Docker with newgrp docker or sudo service docker restart, or restart Ubuntu.
"},{"location":"en/faq.html#operations","title":"Operations","text":""},{"location":"en/faq.html#i-get-a-no-module-named-error-when-creating-a-package-with-python-and-running-it","title":"I get a no module named * error when creating a package with Python and running it.","text":"
Refer to this guide.
"},{"location":"en/faq.html#what-command-should-i-use-to-check-the-type-of-a-topic","title":"What command should I use to check the type of a topic?","text":"
Use ros2 topic info -v fuga_topic to check the type of a topic, or if you can identify the node, use ros2 node info hoge-node. For more information about ROS commands, searching for \"ROS2 commands\" online may also help.
"},{"location":"en/faq.html#maps-and-routes-are-not-displayed-in-rviz","title":"Maps and routes are not displayed in Rviz.","text":"
Ensure that the map data is placed in the correct location and is valid.
"},{"location":"en/faq.html#i-dont-know-how-to-improve-autoware-for-participation","title":"I don't know how to improve Autoware for participation.","text":"
Methods include adjusting parameters, improving nodes, or replacing nodes in Autoware. Basic configurations of Autoware can be found on the website or here. Additionally, this external article might be helpful.
"},{"location":"en/faq.html#please-explain-about-behavior-pathmotion-planner","title":"Please explain about Behavior Path/Motion Planner.","text":"
The behavior planner primarily functions for general roads (ODD3 and above), considering traffic rules like stop lines, crosswalks, and signal stops. It does not optimize avoidance functions. On the other hand, the motion planner functions for limited areas (ODD2 and below), handling basic driving functionalities such as obstacle avoidance, stopping, and speed optimization without using signals or map information.
There are two types of avoidance: behavior path and obstacle avoidance. By default, obstacle avoidance is off and only path smoothing is performed. The default setting is to avoid using the behavior path, but the default avoidance targets are only cars and trucks.
"},{"location":"en/faq.html#please-explain-the-center-point","title":"Please explain the center point.","text":"
The center point detects cars, trucks, and pedestrians, but not untagged objects like cardboard boxes. Currently, Autoware requires object data for planning, and the default configuration using center point can lead to two issues:
If the center point fails, planning cannot generate a path.
Clustering-based obstacle detection results are erased during data association.
Although Autoware mini is the ideal perception configuration, understanding these issues and selectively implementing nodes is challenging. Ensuring the center point functions correctly may be important. Reference
"},{"location":"en/faq.html#awsim","title":"AWSIM","text":""},{"location":"en/faq.html#how-can-i-reset-the-car-to-the-initial-position","title":"How can I reset the car to the initial position?","text":"
Currently, the only way to do this is by restarting AWSIM.
"},{"location":"en/faq.html#awsim-operation-is-unstable","title":"AWSIM operation is unstable.","text":"
This may be due to insufficient GPU performance. If using a high-performance GPU is not feasible, setting the time scale to about 0.5 using the slider at the bottom of the AWSIM screen may stabilize operation.
"},{"location":"en/faq.html#i-want-to-tune-the-mpc-are-the-model-parameters-delay-and-time-constants-used-in-this-awsim-disclosed","title":"I want to tune the MPC. Are the model parameters (delay and time constants) used in this AWSIM disclosed?","text":"
The delay and time constants are neither measured nor disclosed, but the basic specifications are available here.
"},{"location":"en/faq.html#general-competition-questions","title":"General Competition Questions","text":""},{"location":"en/faq.html#is-it-possible-to-add-extra-sensors","title":"Is it possible to add extra sensors?","text":"
To ensure all participants face the same conditions and difficulty, the addition of new sensors is not allowed.
This page outlines the steps to participate in the AI Challenge.
You can participate in this competition with a single PC running Ubuntu 22.04.
First, use the online scoring environment, then proceed with environment setup and development.
"},{"location":"en/getting-started.html#register-for-the-autonomous-driving-ai-challenge-2024","title":"Register for the Autonomous Driving AI Challenge 2024","text":"
Registration for the 2024 competitions have already been closed.
"},{"location":"en/getting-started.html#accessing-and-submitting-to-the-online-scoring-environment","title":"Accessing and Submitting to the Online Scoring Environment","text":"
In this competition, you will upload submission files (compressed source code files) to the online environment, where they will be automatically scored and ranked.
Let's try using the online scoring environment with these four steps!
Info
Accessing the online scoring environment and submitting a file should take about 5 minutes.
After registering for the Autonomous Driving AI Challenge, login information will be sent to your registered email address.
Access the online scoring environment and log in using the credentials provided in the email.
Once you have access, try submitting a source code file. Download the sample code compressed file from the red button below.
Upload the file directly through the \"UPLOAD\" button in the online scoring environment to submit it.
Download the sample code compressed file
"},{"location":"en/getting-started.html#setting-up-the-ai-challenge-environment","title":"Setting Up the AI Challenge Environment","text":"
Please follow the link above to set up the environment.
Info
You can participate in this competition with a single PC running Ubuntu 22.04.
"},{"location":"en/getting-started.html#how-to-proceed-with-development-in-the-ai-challenge","title":"How to Proceed with Development in the AI Challenge","text":"
Let's start developing by following the link above!
"},{"location":"en/getting-started.html#submitting-your-source-code","title":"Submitting Your Source Code","text":"
Submit your completed code via the online scoring environment. Set up your submission using the link above.
This competition is a new initiative aimed at discovering and nurturing engineers who will lead the future automotive industry in the new technological domains known as CASE and MaaS.
The competition involves not only developing programs for autonomous driving mobility but also competing in driving competitions with these developed programs. It aims to provide a platform for engineers, researchers, and students involved in computer science, AI, software, and information processing to challenge themselves, learn, and create organic connections.
"},{"location":"en/index.html#objectives","title":"Objectives","text":""},{"location":"en/index.html#the-role-of-the-competition-from-a-technical-perspective","title":"The Role of the Competition from a Technical Perspective","text":"
Learn SDV (Software Defined Vehicle) development through software integration while understanding hardware
Conduct development using Open Source Software (OSS) as a platform for innovation towards social implementation
"},{"location":"en/index.html#the-role-of-the-competition-in-human-resource-development","title":"The Role of the Competition in Human Resource Development","text":"
Promote participation of engineers from various fields
Accelerate skill development through the provision of educational content
Learn how to develop SDVs by reconciling real machines and simulators
Innovate through digital twin simulations
Create \"aspirations\" and \"passion and excitement\" by combining technical competition with entertainment, using motorsport as a theme
The preliminary round will be conducted through online simulations. The competition aims to achieve faster lap times on the course using AWSIM, which is oriented towards digital twin simulations. Participants will not only learn the structure of Autoware but also adjust parameters for behavior and decision-making parts and develop new algorithms as needed.
The final competition will be conducted using an EV racing kart as the competition vehicle. Participants will apply the knowledge gained from simulations to real vehicles and tackle challenges unique to real vehicles that cannot be replicated in AWSIM.
For example, participants will be challenged to adjust parameters for application to real vehicles and develop algorithms for noise handling and delay countermeasures that cannot be replicated in simulations.
The racing kart will drive around a circuit course and compete for the time it takes to complete a set number of laps. Although the karts will be driving alone this time, in the future they will be driving together with others. Therefore, there is a challenge to avoid virtual objects placed on the course.
For this competition, we have prepared an implementation based on the autonomous driving software Autoware. This page provides background information and explanations on how to utilize this implementation effectively.
In the previous simulation competition, we provided a launch file that could start a reduced configuration of Autoware by limiting functions and reducing the number of nodes from the default Autoware. For the background and purpose of this setup, please refer to the previous competition's documentation.
For this simulation competition, we have similarly prepared a reduced configuration of Autoware designed for use with AWSIM to enable partial use and flexible integration of Autoware.
"},{"location":"en/development/main-module.html#background-of-the-reduced-configuration-of-autoware","title":"Background of the Reduced Configuration of Autoware","text":""},{"location":"en/development/main-module.html#challenges-of-using-autoware","title":"Challenges of Using Autoware","text":"
The default Autoware is composed of many nodes to accommodate various driving environments.
You can also view the configuration diagram of ROS nodes that constitute Autoware in the official Autoware documentation. The current diagram is shown below.
Autoware is equipped with a wide range of functions in each component related to autonomous driving, designed to handle complex driving environments.
However, understanding this complex configuration, the meaning and adjustment of each parameter, and switching or replacing modules is not necessarily easy.
"},{"location":"en/development/main-module.html#preparing-a-reduced-configuration-of-autoware-micro","title":"Preparing a Reduced Configuration of Autoware-Micro","text":"
Therefore, in the previous simulation competition, we prepared a reduced configuration of Autoware by limiting functions and reducing the number of nodes from the default Autoware.
The node diagram of Autoware-Micro is shown below. You can see that the number of nodes has significantly decreased, and only the functions necessary for basic autonomous driving are included.
Features of Autoware-Micro include:
Almost all nodes are started directly from a single launch file.
Parameters are written directly at the node startup, making it easy to track which parameters are needed for which nodes.
The ROS topic names used for input and output of each node are directly remapped at the node startup, making it easy to change the topic names.
By writing autonomous driving software based on this Autoware, you can:
Understand the inner workings of Autoware more deeply due to its simple configuration.
Easily replace Autoware modules with your custom modules to work on functionality improvements.
Clearly see the impact of parameter changes on the overall system operation.
Add existing Autoware nodes that are not included in this version of Autoware.
Changes and features of each component include:
Localization: Self-position estimation using GNSS, IMU, and wheel speed.
Planning: Simplified by omitting behavior_velocity_planner and obstacle_stop_planner, directly outputting a driving trajectory from the output route.
Control: A simple implementation example of control with simple_pure_pursuit.
By utilizing Autoware-Micro, you can focus on the challenges of this competition:
Strategic route planning for curves.
Vehicle control at high speeds.
Moreover, while referring to the implementation example of Autoware-Micro, you can try implementation methods slightly different from Autoware's architecture or create and introduce new custom nodes.
By incorporating your custom nodes, you can improve driving performance and increase your score.
For example, you can consider the following configuration, implement \"Planning\" and \"Control\" separately, or implement a node that handles both \"Planning & Control.\"
You are free to customize as long as the ROS topics for route input and vehicle interface output match.
When there are significant updates to the competition environment, announcements will be made accordingly. For reference, the following instructions are provided.
cd aichallenge2024 # path to aichallenge2024\ngit pull origin/main\n
"},{"location":"en/development/reference.html#installing-awsim-with-visualization","title":"Installing AWSIM with Visualization","text":"
If you want to check the simulation screen of AWSIM, follow the steps in this guide to install AWSIM with visualization.
"},{"location":"en/development/reference.html#setting-up-three-terminals-for-debugging-reference","title":"Setting up Three Terminals for Debugging (Reference)","text":"
To develop with three terminals for debugging, open the first terminal using Alt+Ctrl+T and then execute the following commands by pasting them with Ctrl+Shift+P and pressing Enter.
cd ~/aichallenge-2024\n./docker_run.sh dev cpu\n
cd /aichallenge\nbash run_simulator.bash\n
Open the second terminal using Alt+Ctrl+T and then execute the following commands by pasting them with Ctrl+Shift+P and pressing Enter.
cd ~/aichallenge-2024\n./docker_run.sh dev cpu\n
cd /aichallenge\nbash run_autoware.bash\n
Open the third terminal using Alt+Ctrl+T and then execute the following commands by pasting them with Ctrl+Shift+P and pressing Enter.
cd ~/aichallenge-2024\n./docker_run.sh dev cpu\n
cd /aichallenge\nros2 topic pub --once /control/control_mode_request_topic std_msgs/msg/Bool '{data: true}' >/dev/null\n
When the screen below appears, the startup is complete. To terminate, press CTRL + C in each terminal.
"},{"location":"en/development/workspace-usage.html","title":"How to Proceed with the AI Challenge","text":"
The AI Challenge leverages open-source software. By utilizing the code and web platform provided by the organizers, you can skip the initial development phase and immediately start developing in line with the competition theme. This approach has the significant advantage of avoiding \"reinventing the wheel.\" Additionally, it allows anyone to easily participate in the competition and enables the competition to be run with consistent evaluation criteria.
For first-time participants, you will start from a state where most of the functions necessary for autonomous driving are already in place, standing on the foundation built by your predecessors. From here, you have the opportunity to deepen your unique development in the competition field through the community's \"publication of efforts.\" Furthermore, to deepen your understanding of autonomous driving, we recommend using the \" Autoware Practice \" prepared by the organizers and the learning programs provided by the ROS 2 community, such as \" ROS 2 \".
For those who have already participated in the challenge, we encourage you to share your experiences, contribute to the community, and help the competition evolve. Your active participation will contribute to making the competition even more fulfilling.
The source code that forms the basis for development in the AI Challenge is provided in the competition repository .
Participants will proceed with development by customizing this code and parameters. However, if you are unfamiliar with Autoware, we recommend going through the introductory course first.
For those who want to know the specifications, such as those developing independently without using the repository's code, refer to the interface specifications and simulator specifications pages.
"},{"location":"en/development/workspace-usage.html#read-the-reference-articles-by-voluntary-participants","title":"Read the reference articles by voluntary participants","text":"
The efforts of voluntary participants are summarized in the Advent Calendar , so please refer to them.
If you are unsure where to start, we recommend starting with this article written by Mr. Arata Tanaka, who won the Community Contribution Award in 2023.
"},{"location":"en/development/workspace-usage.html#try-changing-the-parameters","title":"Try changing the parameters","text":"
For those who are unsure what to do after setting up the environment, try adjusting the parameters first. This time, let's change the parameters of the control module simple_pure_pursuit.
Let's adjust the value values below in $HOME/aichallenge-2024/aichallenge/workspace/src/aichallenge_submit/aichallenge_submit_launch/launch/reference.launch.xml.
After customizing the workspace, refer to this to submit.
"},{"location":"en/development/workspace-usage.html#next-step-learn-about-the-main-module","title":"Next Step: Learn about the Main Module","text":""},{"location":"en/information/rules.html","title":"Rules","text":""},{"location":"en/information/rules.html#overview","title":"Overview","text":"
Teams will compete to achieve the shortest driving time while completing the specified number of laps on a designated course.
The course will have a \"Start Area,\" \"Control Line,\" and \"Pit Stop Area.\" Vehicles will start from the Start Area, and the driving time will be measured when they touch the Control Line. For details on the Pit Stop Area, refer to the \"Pit Stop\" section below. Each team will drive individually, without other vehicles or obstacles on the course simultaneously.
Each team will have a preparation session to set up their vehicle and a recording session to measure driving times. However, in the preliminary competition, vehicles will not be used, so there will be no preparation session. Advanced class teams can always perform vehicle maintenance, so they do not have a preparation session either.
Item Final Competition Preliminary Competition Preparation Session TBD None Recording Session TBD 7:00 Number of Laps TBD 6"},{"location":"en/information/rules.html#starting-the-drive","title":"Starting the Drive","text":"
Vehicles will start from the Start Area, and the driving time will begin when they first touch the Control Line. In the preliminary competition, vehicles will be pre-positioned in a predetermined posture. In the final competition, vehicles can be placed in any posture within the Start Area, but operations on the vehicle are only allowed within the Start Area.
"},{"location":"en/information/rules.html#ending-the-drive","title":"Ending the Drive","text":"
The drive will end and be recorded as a result under the following conditions:
The specified number of laps is completed.
The allotted time for the recording session has elapsed.
The vehicle is touched and operated.
Any other reason deemed appropriate by the organizers.
"},{"location":"en/information/rules.html#stopping-the-drive","title":"Stopping the Drive","text":"
The drive will end and be invalidated under the following conditions:
(Preliminary only) The vehicle has not passed the Control Line within 2 minutes from the start of the recording session.
(Preliminary only) The vehicle has significantly deviated from the course.
The course walls are moved.
Any other reason deemed appropriate by the organizers.
Vehicles have a virtual value called \"Condition,\" which, when increased, restricts their speed. Condition increases as the vehicle drives and also when it collides with virtual obstacles described below. The Condition can be reset to its initial value by stopping in the Pit Stop Area for a specified number of seconds.
Setting Item Value Additional Notes Pit Stop Time 3.0 seconds \u2015 Speed Limit Activation 1000 Maximum speed is limited to 20 km/h Section Pass 30 \u2015 Virtual Obstacle Collision 20 - 380 Varies depending on the collision"},{"location":"en/information/rules.html#pit-stop-area","title":"Pit Stop Area","text":"
The Pit Stop Area is indicated by a green frame as shown in the image below.
The course is virtually divided into multiple sections, and Condition increases by a fixed amount each time the vehicle exits a section. Additionally, virtual obstacles displayed with a purple frame, as shown in the image below, are placed on the course, and Condition increases if the vehicle collides with them (virtual obstacles do not affect the physical behavior of the vehicle).
Virtual obstacles are generated at random positions within a section each time the vehicle exits a section. After the first lap, virtual obstacles are removed and regenerated in the section, so multiple virtual obstacles will not be placed within the same section. Also, no virtual obstacles are generated near the Pit Stop Area.
"},{"location":"en/information/schedule.html","title":"Competition Information","text":""},{"location":"en/information/schedule.html#overall-flow","title":"Overall Flow","text":""},{"location":"en/information/schedule.html#schedule","title":"Schedule","text":"Event Date Participant Registration May 27, 2024 - July 1, 2024 Networking Event June 21, 2024 Preliminary Round July 2, 2024 - September 2, 2024 Preliminary Awards Ceremony Around September 2024 (tentative) Practice Day October 10-11, 2024 Practice Day November 1, 2024 Semifinals November 2, 2024 Finals November 3, 2024 Finals Awards Ceremony & Networking Event Around December 2024"},{"location":"en/preliminaries/check-results.html","title":"Checking Results","text":"
This page explains the rules and ranking system for the competition. Please note that the content of this page may change during the competition period.
Scores will be calculated based on the following steps. If multiple runs are made, the higher score will be adopted. If a run is stopped, it will be treated as having completed 0 laps.
The number of laps completed at the end of the run.
The shortest total lap time up to the final lap.
Special Awards: Preliminary rounds will have a seeding system, and finals will have awards.
Best Lap Time: Measured using SIM in the preliminaries and TOM\u2019S system in the finals.
Best Comfortable Ride: Measured using SIM in the preliminaries and by measuring the water in a glass in the finals.
Interaction and recognition of engineers from various fields.
In this competition, scoring will be conducted using an online environment equipped with a simulator and automatic scoring functions. Please follow the steps below to upload your created packages to the online environment. Once uploaded, the simulation will automatically start, and the results will be displayed.
Submit your work by following these steps:
Compress the source code.
Verify the operation in the local evaluation environment.
Submit to the online scoring environment.
"},{"location":"en/preliminaries/submission.html#upload-procedure-to-the-online-environment","title":"Upload Procedure to the Online Environment","text":"
Operation Verification
1.1. Preparation
Compress aichallenge_submit and generate a folder for result output.
Run: ./create_submit_file.bash
1.2. Build Docker Image
Run: ./docker_build.sh eval
1.3. Start Docker Container
After startup, Autoware will automatically launch, and autonomous driving will begin.
For GPU version of AWSIM: ./docker_run.sh eval gpu
For CPU version of AWSIM: ./docker_run.sh eval cpu
1.4. Check result.json
After evaluation is complete, the following files will be stored in the output/latest folder:
autoware.log
rosbag2_autoware
capture
result-summary.json
result-details.json
Upload to the Online Environment
Access the online environment and log in from the \u201cLOG IN\u201d button in the top right corner.
Once logged in, upload aichallenge_submit.tar.gz from the green \u201cUPLOAD\u201d button. After uploading, the source code will be built, and the simulation will be executed in sequence.
If it completes successfully, it will display \"Scoring Completed,\" and you can download result.json, with each lap time displayed.
If the scenario execution fails, such as a launch failure, resulting in no score output, it will display \"No Results.\" In this case, please re-upload, as it might be an internal server error. Contact support if the problem persists.
If the build fails, it will display \"Build Failed.\" Check the steps and re-upload.
The highest score from all previous scorings will be applied to the ranking.
You cannot upload new source code while scoring is in progress.
You can upload up to 10 times per day, with the count reset at midnight Japan time.
Check Results
After evaluation in the online environment, you can download result.json. Download and check the results.
If There Are No Results
4.1. Check for package dependency issues
Verify that there are no missing dependencies in package.xml, setup.py, or CMakeLists.txt, depending on the language used.
4.2. Check Docker
Check inside Docker with the following command to ensure everything is correctly installed and built in the required directories.
Run: docker run -it aichallenge-2024-eval:latest /bin/bash
Verify the following directories:
/aichallenge/workspace/*
/autoware/install/*
"},{"location":"en/setup/build-docker.html","title":"Building and Running the Competition Repository","text":"
In the competition repository, the actual runtime environment is provided entirely within Docker. The steps to use the repository are as follows:
Build the Docker image for the competition environment
Build Autoware within the Docker container
Run Autoware and the simulator simultaneously within the Docker container
"},{"location":"en/setup/build-docker.html#building-the-docker-image-for-the-competition-environment","title":"Building the Docker Image for the Competition Environment","text":"
Open a terminal again using Alt+Ctrl+T. Follow the commands below by pasting them with Ctrl+Shift+P and pressing Enter.
First, navigate to the competition repository.
cd ~/aichallenge-2024\n
Build the Docker image.
./docker_build.sh dev\n
Check if the following image has been generated by running:
docker images\n
You should see an image like this:
aichallenge-2024-dev latest df2e83a20349 33 minutes ago 8.9GB\n
"},{"location":"en/setup/build-docker.html#building-autoware-within-the-docker-container","title":"Building Autoware within the Docker Container","text":"
Start the Docker container by executing the following:
cd ~/aichallenge-2024\n./docker_run.sh dev cpu\n
Although it may not look like anything has changed, the above command moves the environment inside the Docker container. To confirm that you are inside the Docker container, you can run the following command and check if nothing is displayed.
ls ~\n
Within the Docker container, build Autoware by executing:
cd /aichallenge\n./build_autoware.bash\n
"},{"location":"en/setup/build-docker.html#running-autoware-and-the-simulator-within-the-docker-container","title":"Running Autoware and the Simulator within the Docker Container","text":"
After building Autoware, run the following command:
./run_evaluation.bash\n
When the screen shown below appears, the startup is complete. To terminate, press CTRL + C in the terminal.
This concludes the environment setup! Next, let's proceed to actual development.
"},{"location":"en/setup/docker.html","title":"Installing the Virtual Environment","text":""},{"location":"en/setup/docker.html#installing-dependencies","title":"Installing Dependencies","text":"
"},{"location":"en/setup/docker.html#obtaining-the-docker-image-for-the-autoware-environment","title":"Obtaining the Docker Image for the Autoware Environment","text":"
Download the Docker image for the Autoware environment used in the AI Challenge.
The Docker image is approximately 10GB in size, so it is recommended to use a wired LAN for downloading.
For first-time users, proceed to the documentation for headless AWSIM. If you have a PC with a GPU and want a richer development environment, proceed to the documentation for AWSIM with visualization.
Download the latest AWSIM_GPU_**.zip file from Google Drive and extract it to aichallenge-2024/aichallenge/simulator.
Confirm that the executable file exists at aichallenge-2024/aichallenge/simulator/AWSIM/AWSIM.x86_64.
Change the permissions as shown in the diagram.
With this, the environment setup is complete!
"},{"location":"en/setup/headless-simulation.html#next-step-building-and-running-the-competition-repository","title":"Next Step: Building and Running the Competition Repository","text":""},{"location":"en/setup/requirements.html","title":"Recommended Environment","text":"
For the PC used in this competition, we recommend the following specifications. While it is possible to run with lower specifications, it may result in unstable execution speeds on the ROS 2 side, causing significant variations in behavior during simulations.
Warning
If you only have a Windows environment, please install Ubuntu 22.04. While it is possible to install Ubuntu on the same disk as your Windows environment, if you are not familiar with the process, you may accidentally damage your Windows environment. Therefore, we strongly recommend purchasing a new external or internal SSD and installing Ubuntu there.
Info
For guidance on installing Ubuntu, this article may be helpful.
CPU: Intel Core i5 (4 cores) or higher (recommended)
Memory:
Minimum: 8 GB
Recommended: 16 GB or more
SSD: 60 GB or more
"},{"location":"en/setup/requirements.html#using-awsim-with-visualization","title":"Using AWSIM with Visualization","text":"
OS: Ubuntu 22.04
CPU: Intel Core i7 (8 cores) or higher
GPU: NVIDIA GeForce with 8 GB VRAM
Memory: 16 GB or more
Storage: SSD 60 GB or more
"},{"location":"en/setup/requirements.html#next-step-clone-the-workspace","title":"Next Step: Clone the Workspace","text":""},{"location":"en/setup/visible-simulation.html","title":"Downloading AWSIM with Visualization (Reference)","text":"
By default, we distribute a headless version of AWSIM, but we also provide instructions for setting up an environment with visualization for those who wish to use it. Note that setting up a GPU-based environment can often lead to issues, so if you cannot meet the recommended environment specifications or if this is your first time participating, please consider this as a reference.
If you are using AWSIM with visualization, start the container with the following commands:
cd aichallenge-2024\n./docker_build.sh dev\n./docker_run.sh dev gpu\n
Within the terminal where the container is running (inside the container), execute the following:
cd /aichallenge\n./build_autoware.bash\n
After building Autoware, modify run_simulator.bash. Specify the directory you just extracted for AISIM_GPU_**.
#!/bin/bash\n\n# shellcheck disable=SC1091\nsource /aichallenge/workspace/install/setup.bash\nsudo ip link set multicast on lo\n/aichallenge/simulator/AWSIM_GPU_**/AWSIM.x86_64\n
Make the following changes to run_evaluation.bash as well.
"},{"location":"en/setup/workspace-setup.html","title":"Cloning the Workspace","text":""},{"location":"en/setup/workspace-setup.html#installing-dependencies","title":"Installing Dependencies","text":"
Open a terminal with Alt+Ctrl+T, then paste the following commands using Ctrl+Shift+P and press Enter. First, install the necessary libraries.
sudo apt update\nsudo apt install -y git\n
"},{"location":"en/setup/workspace-setup.html#cloning-the-competition-repository","title":"Cloning the Competition Repository","text":"
Clone the workspace repository. Here, we specify the home directory, but you can place it in any directory of your choice.
cd ~\ngit clone https://github.com/AutomotiveAIChallenge/aichallenge-2024.git\n
"},{"location":"en/setup/workspace-setup.html#next-step-installing-the-virtual-environment","title":"Next Step: Installing the Virtual Environment","text":""},{"location":"en/specifications/hardware.html","title":"Hardware","text":""},{"location":"en/specifications/interface.html","title":"Interface","text":""},{"location":"en/specifications/interface.html#list","title":"List","text":"Interface Name Type Service /control/control_mode_requestautoware_auto_vehicle_msgs/srv/ControlModeCommand Publisher /vehicle/status/control_modeautoware_auto_vehicle_msgs/msg/ControlModeReport Subscription /control/command/control_cmdautoware_auto_control_msgs/msg/AckermannControlCommand Publisher /vehicle/status/velocity_statusautoware_auto_vehicle_msgs/msg/VelocityReport Publisher /vehicle/status/steering_statusautoware_auto_vehicle_msgs/msg/SteeringReport Subscription /control/command/gear_cmdautoware_auto_vehicle_msgs/msg/GearCommand Publisher /vehicle/status/gear_statusautoware_auto_vehicle_msgs/msg/GearReport Publisher /sensing/gnss/pose_with_covariancegeometry_msgs/msg/PoseWithCovarianceStamped Publisher /sensing/imu/imu_rawsensor_msgs/msg/Imu Publisher /aichallenge/objectssstd_msgs/msg/Float64MultiArray Publisher /aichallenge/pitstop/areastd_msgs.msg.Float64MultiArray Publisher /aichallenge/pitstop/conditionstd_msgs.msg.Int32 Publisher /aichallenge/pitstop/statusstd_msgs.msg.Float32"},{"location":"en/specifications/interface.html#controlcommandcontrol_cmd","title":"/control/command/control_cmd","text":"Name Description stamp Message timestamp lateral.stamp Unused lateral.steering_tire_angle Target steering angle lateral.steering_tire_rotation_rate Unused longitudinal.stamp Unused longitudinal.speed Unused longitudinal.acceleration Target acceleration longitudinal.jerk Unused"},{"location":"en/specifications/interface.html#vehiclestatusvelocity_status","title":"/vehicle/status/velocity_status","text":"Name Description header.stamp Data acquisition time header.frame_id Frame ID (base_link) longitudinal_velocity Longitudinal velocity lateral_velocity Lateral velocity heading_rate Angular velocity"},{"location":"en/specifications/interface.html#vehiclestatussteering_status","title":"/vehicle/status/steering_status","text":"Name Description stamp Data acquisition time steering_tire_angle Steering angle"},{"location":"en/specifications/interface.html#controlcommandgear_cmd","title":"/control/command/gear_cmd","text":"Name Description stamp Message timestamp command Gear type"},{"location":"en/specifications/interface.html#vehiclestatusgear_status","title":"/vehicle/status/gear_status","text":"Name Description stamp Data acquisition time report Gear type"},{"location":"en/specifications/interface.html#sensinggnsspose_with_covariance","title":"/sensing/gnss/pose_with_covariance","text":"Name Description header.stamp Data acquisition time header.frame_id Frame ID (map) pose.pose.position Vehicle position (origin of base_link) pose.pose.orientation Unused pose.covariance Position accuracy"},{"location":"en/specifications/interface.html#sensingimuimu_raw","title":"/sensing/imu/imu_raw","text":"Name Description header.stamp Data acquisition time header.frame_id Frame ID (imu_link) orientation Orientation angular_velocity Angular velocity linear_acceleration Linear acceleration"},{"location":"en/specifications/interface.html#aichallengeobjects","title":"/aichallenge/objects","text":"Name Description data[N * 4 + 0] X coordinate of Nth object data[N * 4 + 1] Y coordinate of Nth object data[N * 4 + 2] Z coordinate of Nth object data[N * 4 + 3] Radius of Nth object"},{"location":"en/specifications/interface.html#aichallengepitstoparea","title":"/aichallenge/pitstop/area","text":"Name Description data[0] X position of Pit Stop Area data[1] Y position of Pit Stop Area data[2] Z position of Pit Stop Area data[3] X quaternion of Pit Stop Area data[4] Y quaternion of Pit Stop Area data[5] Z quaternion of Pit Stop Area data[6] W quaternion of Pit Stop Area data[7] X size of of Pit Stop Area data[8] Y size of of Pit Stop Area"},{"location":"en/specifications/interface.html#aichallengepitstopcondition","title":"/aichallenge/pitstop/condition","text":"Name Description data Current condition value"},{"location":"en/specifications/interface.html#aichallengepitstopstatus","title":"/aichallenge/pitstop/status","text":"Name Description data Number of seconds a pit stop is valid"},{"location":"en/specifications/simulator.html","title":"Simulator","text":""},{"location":"en/specifications/simulator.html#overview","title":"Overview","text":"
This page describes the specifications of the simulator used in the AI Challenge.
The simulator is based on the open-source autonomous driving simulator \"AWSIM\" developed for Autoware.
"},{"location":"en/specifications/simulator.html#commandline-options","title":"Commandline Options","text":"Option Type Default Description --timeout float 420.0 Set session timeout seconds. --endless bool false Enable/disable session timeout. --pit-stop bool true Enable/disable features related to pit-stop. --replay0 string Load driving logs and replay as a different vehicle.
Use result-details.json for the driving log for replay. Also, replay supports 10 vehicles from --replay0 to --replay9.
"},{"location":"en/specifications/simulator.html#keyboard-operation","title":"Keyboard Operation","text":"Operation Key Quit Esc Reset Space Switch camera C Accel Arrow Up Brake Arrow Down Steering Arrow Left, Right Gear (D) D Gear (R) R Gear (N) N Gear (P) P"},{"location":"en/specifications/simulator.html#topic-operation","title":"Topic Operation","text":"Topic Type Description /aichallenge/awsim/status std_msgs.msg.Float32MultiArray Get status of the simulation. /aichallenge/awsim/change_time_scale std_msgs.msg.Float32 Set the timescale for the simulation. /aichallenge/awsim/reset std_msgs.msg.Empty Reset the simulation.
The above /aichallenge/awsim/status has the following structure.
Index Value 0 session timeout 1 lap count 2 lap time 3 section 4 timescale"},{"location":"en/specifications/simulator.html#vehicle-racing-kart","title":"Vehicle (Racing Kart)","text":"
The vehicle conforms to the specifications of the EGO Vehicle in AWSIM and is designed with specifications close to an actual racing kart.
The following table summarizes the vehicle parameters.
Item Value Vehicle Weight 160 kg Length 200 cm Width 145 cm Front Wheel Diameter 24 cm Front Wheel Width 13 cm Front Wheel Tread 93 cm Rear Wheel Diameter 24 cm Rear Wheel Width 18 cm Rear Wheel Tread 112 cm Maximum Steering Angle 80\u00b0 Maximum Acceleration 3.2 m/s^2"},{"location":"en/specifications/simulator.html#vehicle-component","title":"Vehicle Component","text":"
The following table summarizes the settings of the Vehicle component. For detailed information of the setting items, see this manual.
Item Value Vehicle Settings Use Inertia Off Physics Settings (experimental) Sleep Velocity Threshold 0.02 Sleep Time Threshold 0 Skidding Cancel Rate 0.236 Input Settings Max Steer Angle Input 80 Max Acceleration Input 3.2"},{"location":"en/specifications/simulator.html#rigidbody-component","title":"Rigidbody Component","text":"
The following table summarizes the settings of the Rigidbody component. For more information, see this manual.
Item Value Mass 160 Drag 0 Angular Drag 0"},{"location":"en/specifications/simulator.html#com-position","title":"CoM Position","text":"
CoM (Center of Mass) is the mass center of the vehicle Rigidbody. The CoM position is set at the center of the vehicle and at the height of the wheel axles.
Vehicle collider is used to detect collision between the vehicle and other objects or checkpoints. The vehicle collider is created based on the mesh of the vehicle object.
The vehicle has a total of four wheel colliders - one for each wheel, simulating the vehicle on a four-wheel model, rather than a kinematic bicycle model.
The Wheel Collider is set as follows. For more details on wheel colliders, please refer to this manual.
Item Value Mass 1 Radius 0.12 Wheel Damping Rate 0.25 Suspension Distance 0.001 Suspension Spring Spring (N/m) 35000 Damper (N*s/m) 3500 Target Position 0.01"},{"location":"en/specifications/simulator.html#sensor-configuration","title":"Sensor Configuration","text":""},{"location":"en/specifications/simulator.html#gnss","title":"GNSS","text":"
The GNSS is mounted at the following position relative to the vehicle base link.
Item Value x 0.0 m y 0.0 m z 0.0 m roll 0.0 rad pitch 0.0 rad yaw 0.0 rad"},{"location":"en/specifications/simulator.html#imu","title":"IMU","text":"
The IMU is mounted at the following position relative to the vehicle base link.
Item Value x 0.0 m y 0.0 m z 0.0 m roll 0.0 rad pitch 0.0 rad yaw 0.0 rad"}]}
\ No newline at end of file
+{"config":{"lang":["en","ja"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"community.html","title":"Community","text":""},{"location":"community.html#ai-2024","title":"\u81ea\u52d5\u904b\u8ee2AI\u30c1\u30e3\u30ec\u30f3\u30b8\u30a2\u30c9\u30d9\u30f3\u30c8\u30ab\u30ec\u30f3\u30c0\u30fc 2024","text":"
"},{"location":"faq.html#_1","title":"\u74b0\u5883\u69cb\u7bc9","text":""},{"location":"faq.html#awsim-and-autoware","title":"AWSIM and Autoware\u9593\u306e\u901a\u4fe1\u304c\u5b89\u5b9a\u3057\u307e\u305b\u3093\u3002","text":"
local \u3067\u30c6\u30b9\u30c8\u3059\u308b\u969b\u3001\u3059\u3079\u3066\u306e terminal \u3067ROS_LOCALHOST_ONLY=1\u306b\u8a2d\u5b9a\u3059\u308b\u3068\u901a\u4fe1\u901f\u5ea6\u304c\u5411\u4e0a\u3057\u307e\u3059\u3002 .bashrc \u306b\u4ee5\u4e0b\u306e\u884c\u3092\u8ffd\u52a0\u3057\u3066\u304f\u3060\u3055\u3044\u3002
export ROS_LOCALHOST_ONLY=1\nexport RMW_IMPLEMENTATION=rmw_cyclonedds_cpp\n\nif [ ! -e /tmp/cycloneDDS_configured ]; then\n sudo sysctl -w net.core.rmem_max=2147483647\n sudo ip link set lo multicast on\n touch /tmp/cycloneDDS_configured\n
OS \u306e\u8d77\u52d5\u5f8c\u3001\u30bf\u30fc\u30df\u30ca\u30eb\u306e\u8d77\u52d5\u6642\u306b\u30d1\u30b9\u30ef\u30fc\u30c9\u304c\u8981\u6c42\u3055\u308c\u3001\u521d\u56de\u306b\u306f sudo ip link set lo multicast on \u304c\u5fc5\u8981\u3067\u3059\u3002
"},{"location":"faq.html#windowsawsimubuntuautoware-ros2-topic-list","title":"Windows\u306eAWSIM\u3068Ubuntu\u306eAutoware\u3092\u4f7f\u7528\u3057\u3066\u304a\u308a\u3001$ ros2 topic list \u304c\u8868\u793a\u3055\u308c\u307e\u305b\u3093\u3002","text":"
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"},{"location":"faq.html#warning-unable-to-detect-os-for-base-image-aichallenge-2024-dev-maybe-the-base-image-does-not-exist","title":"WARNING unable to detect os for base image 'aichallenge-2024-dev', maybe the base image does not exist\u304c\u51fa\u307e\u3059\u3002","text":"
newgrp docker\u304bsudo service docker restart\u3067docker\u306e\u518d\u8d77\u52d5\u307e\u305f\u306fUbuntu\u306e\u518d\u8d77\u52d5\u3092\u304a\u9858\u3044\u3057\u307e\u3059\u3002
"},{"location":"faq.html#_2","title":"\u64cd\u4f5c","text":""},{"location":"faq.html#ros","title":"ROS","text":""},{"location":"faq.html#python-no-module-named-error","title":"python\u3067\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u4f5c\u6210\u3059\u308b\u3068\u5b9f\u884c\u6642 no module named * \u306eerror\u304c\u8d77\u304d\u307e\u3059\u3002","text":"
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"},{"location":"index.html","title":"Japan Automotive AI Challenge 2024","text":""},{"location":"index.html#_1","title":"\u30b3\u30f3\u30bb\u30d7\u30c8","text":"
State lattice planner\u3068\u306f\u3001\u8eca\u4e21\u306e\u73fe\u5728\u306e\u72b6\u614b\u3068\u76ee\u6a19\u72b6\u614b\u306e\u9593\u306b\u4e00\u9023\u306e\u8ecc\u9053\u5019\u88dc\u3092\u751f\u6210\u3057\u3001\u305d\u308c\u305e\u308c\u306e\u8ecc\u9053\u3092\u8a55\u4fa1\u3057\u3066\u6700\u9069\u306a\u7d4c\u8def\u3092\u9078\u629e\u3059\u308b\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3067\u3059\u3002 \u4ee5\u4e0b\u306e\u753b\u50cf\u306b\u8ecc\u9053\u3092\u751f\u6210\u3059\u308b\u30d5\u30ed\u30fc\u3092\u793a\u3057\u307e\u3059\u3002
state lattice planner\u306e\u30d5\u30ed\u30fc"},{"location":"course/avoidance.html#1","title":"1. \u76ee\u6a19\u72b6\u614b\u3092\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0","text":"
pure pursuit\u306f\u3001\u8eca\u4e21\u306e\u73fe\u5728\u4f4d\u7f6e\u3068\u76ee\u6a19\u7d4c\u8def\u4e0a\u306e\u8ffd\u5f93\u70b9\uff08\u30eb\u30c3\u30af\u30a2\u30d8\u30c3\u30c9\u30dd\u30a4\u30f3\u30c8\uff09\u3068\u306e\u8ddd\u96e2\u3068\u65b9\u5411\u3092\u57fa\u306b\u30eb\u30c3\u30af\u30a2\u30d8\u30c3\u30c9\u30dd\u30a4\u30f3\u30c8\u306b\u5230\u9054\u3059\u308b\u305f\u3081\u306e\u66f2\u7387\u3092\u8a08\u7b97\u3059\u308b\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3067\u3059\u3002\u4ee5\u4e0b\u306bpure pursuit\u306e\u57fa\u672c\u7684\u306a\u52d5\u4f5c\u3092\u8aac\u660e\u3057\u307e\u3059\u3002
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Best Lap Time : \u4e88\u9078\u306fSIM\u3067\u8a08\u6e2c\u3001\u6c7a\u52dd\u306fTOM\u2019S\u306e\u30b7\u30b9\u30c6\u30e0\u3092\u4f7f\u7528
Best Comfortable Ride\uff1a\u4e88\u9078\u306fSIM\u3067\u8a08\u6e2c\u3001\u6c7a\u52dd\u306f\u6c34\u3092\u30b0\u30e9\u30b9\u306b\u5165\u308c\u3066\u8a08\u91cf\u3059\u308b\u3053\u3068\u3067\u5bfe\u5fdc
Hello from Docker!\u3068\u8868\u793a\u3055\u308c\u308c\u3070\u6b63\u5e38\u306b\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u51fa\u6765\u3066\u3044\u307e\u3059\u3002
#!/bin/bash\n\n# shellcheck disable=SC1091\nsource /aichallenge/workspace/install/setup.bash\nsudo ip link set multicast on lo\n/aichallenge/simulator/AWSIM_GPU_**/AWSIM.x86_64\n
\u9805\u76ee \u5024 \u8eca\u4e21\u91cd\u91cf 160 kg \u5168\u9577 200 cm \u5168\u5e45 145 cm \u30db\u30a4\u30fc\u30eb\u30d9\u30fc\u30b9 108.7 cm \u524d\u8f2a\u30bf\u30a4\u30e4\u76f4\u5f84 24 cm \u524d\u8f2a\u30bf\u30a4\u30e4\u5e45 13 cm \u524d\u8f2a\u30db\u30a4\u30fc\u30eb\u30c8\u30ec\u30c3\u30c9 93 cm \u5f8c\u8f2a\u30bf\u30a4\u30e4\u76f4\u5f84 24 cm \u5f8c\u8f2a\u30bf\u30a4\u30e4\u5e45 18 cm \u5f8c\u8f2a\u30db\u30a4\u30fc\u30eb\u30c8\u30ec\u30c3\u30c9 112 cm \u6700\u5927\u30b9\u30c6\u30a2\u30ea\u30f3\u30b0\u8ee2\u8235\u89d2 80 \u00b0 \u99c6\u52d5\u6642\u6700\u5927\u52a0\u901f\u5ea6 3.2 m/s^2"},{"location":"specifications/simulator.html#vehicle","title":"Vehicle\u30b3\u30f3\u30dd\u30fc\u30cd\u30f3\u30c8","text":"
\u9805\u76ee \u5024 Mass 160 Drag 0 Angular Drag 0"},{"location":"specifications/simulator.html#com","title":"CoM\u4f4d\u7f6e","text":"
CoM(Center of Mass)\u306f\u3001\u8eca\u4e21Rigidbody\u306e\u8cea\u91cf\u4e2d\u5fc3\u3067\u3059\u3002CoM\u4f4d\u7f6e\u306f\u3001\u8eca\u4e21\u306e\u4e2d\u5fc3\u304b\u3064\u8eca\u8f2a\u8ef8\u306e\u9ad8\u3055\u306b\u8a2d\u5b9a\u3055\u308c\u3066\u3044\u307e\u3059\u3002
\u9805\u76ee \u5024 x 0.0 m y 0.0 m z 0.0 m roll 0.0 rad pitch 0.0 rad yaw 0.0 rad"},{"location":"specifications/simulator.html#imu","title":"IMU","text":"
\u9805\u76ee \u5024 x 0.0 m y 0.0 m z 0.0 m roll 0.0 rad pitch 0.0 rad yaw 0.0 rad"},{"location":"en/community.html#autonomous-driving-ai-challenge-advent-calendar-2024","title":"Autonomous Driving AI Challenge Advent Calendar 2024","text":"
"},{"location":"en/community.html#articles-on-autonomous-driving-ai-challenge-efforts","title":"Articles on Autonomous Driving AI Challenge Efforts","text":"
Many issues can be resolved using ChatGPT or Google search. For questions that cannot be resolved, please include and attach excerpts of error logs.
"},{"location":"en/faq.html#environment-setup","title":"Environment Setup","text":""},{"location":"en/faq.html#the-communication-between-awsim-and-autoware-is-unstable","title":"The communication between AWSIM and Autoware is unstable.","text":"
When testing locally, setting ROS_LOCALHOST_ONLY=1 in all terminals can improve communication speed. Add the following lines to your .bashrc.
export ROS_LOCALHOST_ONLY=1\nexport RMW_IMPLEMENTATION=rmw_cyclonedds_cpp\n\nif [ ! -e /tmp/cycloneDDS_configured ]; then\n sudo sysctl -w net.core.rmem_max=2147483647\n sudo ip link set lo multicast on\n touch /tmp/cycloneDDS_configured\nfi\n
For a dual-PC setup (Windows+Linux or Linux+Linux), set ROS_LOCALHOST_ONLY=0.
Note:
After OS startup, you will need to enter the password when starting the terminal and execute sudo ip link set lo multicast on for the first time.
Always track changes by using commands like echo $ROS_LOCALHOST_ONLY to avoid forgetting modifications in .bashrc.
Mixed use of ROS_LOCALHOST_ONLY=1 and ROS_LOCALHOST_ONLY=0 will prevent container communication.
Ensure that ROS_LOCALHOST_ONLY is not hard-coded in the executable.
"},{"location":"en/faq.html#ros2-topic-list-does-not-display","title":"ros2 topic list does not display.","text":"
Ensure that the ROS_DOMAIN_ID matches on your machine (this is not an issue if you haven't set ROS_DOMAIN_ID). Also, ensure ROS2 is sourced correctly.
"},{"location":"en/faq.html#using-awsim-on-windows-and-autoware-on-ubuntu-ros2-topic-list-does-not-display","title":"Using AWSIM on Windows and Autoware on Ubuntu, ros2 topic list does not display.","text":"
Allow communication through the Windows Firewall. Also, execute ros2 daemon stop and ros2 daemon start to ensure no unnecessary processes are running, then restart.
"},{"location":"en/faq.html#rocker-does-not-start","title":"Rocker does not start.","text":"
First, verify that Rocker is installed. If it is installed but does not start, check the permissions. It has been reported that differing account types and permissions when building and running the image can cause issues.
"},{"location":"en/faq.html#awsim-terminates-with-a-core-dump","title":"AWSIM terminates with a core dump.","text":"
If AWSIM terminates with a core dump immediately after startup, your GPU may be out of memory. Check the GPU memory usage with nvidia-smi to ensure it is not at its limit. A GPU with at least 11GB of memory is recommended.
"},{"location":"en/faq.html#i-only-have-a-windows-pc-with-a-gpu","title":"I only have a Windows PC with a GPU.","text":"
The official support is for the configuration listed on the HP website, so detailed guidance cannot be provided, but generally, the following methods are possible:
The key is to \"prepare an environment to run Autoware,\" which may involve issues related to performance, package availability, and host-container communication settings. Possible solutions include:
Setting up Ubuntu in a dual-boot configuration.
Using a VM on Windows to run Ubuntu (Hyper-V, VirtualBox, VMware, etc.).
Setting up Ubuntu on WSL2.
Setting up a Docker environment on Windows and running the Autoware image directly.
Building the environment in the cloud (some past participants used AWS).
"},{"location":"en/faq.html#awsim-appears-but-rviz-shows-a-black-screen-when-set-up-on-aws","title":"AWSIM appears but Rviz shows a black screen when set up on AWS.","text":"
There have been cases where running sudo apt upgrade resolved the issue. Additionally, there is a similar question in a past issue that might be helpful.
"},{"location":"en/faq.html#docker_runsh-line-35-rocker-command-not-found-appears","title":"docker_run.sh: line 35: rocker: command not found appears.","text":"
Please install Rocker as described here.
"},{"location":"en/faq.html#warning-unable-to-detect-os-for-base-image-aichallenge-2024-dev-maybe-the-base-image-does-not-exist-appears","title":"WARNING unable to detect os for base image 'aichallenge-2024-dev', maybe the base image does not exist appears.","text":"
Please build the Docker image.
"},{"location":"en/faq.html#unable-to-pull-docker","title":"Unable to pull Docker.","text":"
Please restart Docker with newgrp docker or sudo service docker restart, or restart Ubuntu.
"},{"location":"en/faq.html#operations","title":"Operations","text":""},{"location":"en/faq.html#i-get-a-no-module-named-error-when-creating-a-package-with-python-and-running-it","title":"I get a no module named * error when creating a package with Python and running it.","text":"
Refer to this guide.
"},{"location":"en/faq.html#what-command-should-i-use-to-check-the-type-of-a-topic","title":"What command should I use to check the type of a topic?","text":"
Use ros2 topic info -v fuga_topic to check the type of a topic, or if you can identify the node, use ros2 node info hoge-node. For more information about ROS commands, searching for \"ROS2 commands\" online may also help.
"},{"location":"en/faq.html#maps-and-routes-are-not-displayed-in-rviz","title":"Maps and routes are not displayed in Rviz.","text":"
Ensure that the map data is placed in the correct location and is valid.
"},{"location":"en/faq.html#i-dont-know-how-to-improve-autoware-for-participation","title":"I don't know how to improve Autoware for participation.","text":"
Methods include adjusting parameters, improving nodes, or replacing nodes in Autoware. Basic configurations of Autoware can be found on the website or here. Additionally, this external article might be helpful.
"},{"location":"en/faq.html#please-explain-about-behavior-pathmotion-planner","title":"Please explain about Behavior Path/Motion Planner.","text":"
The behavior planner primarily functions for general roads (ODD3 and above), considering traffic rules like stop lines, crosswalks, and signal stops. It does not optimize avoidance functions. On the other hand, the motion planner functions for limited areas (ODD2 and below), handling basic driving functionalities such as obstacle avoidance, stopping, and speed optimization without using signals or map information.
There are two types of avoidance: behavior path and obstacle avoidance. By default, obstacle avoidance is off and only path smoothing is performed. The default setting is to avoid using the behavior path, but the default avoidance targets are only cars and trucks.
"},{"location":"en/faq.html#please-explain-the-center-point","title":"Please explain the center point.","text":"
The center point detects cars, trucks, and pedestrians, but not untagged objects like cardboard boxes. Currently, Autoware requires object data for planning, and the default configuration using center point can lead to two issues:
If the center point fails, planning cannot generate a path.
Clustering-based obstacle detection results are erased during data association.
Although Autoware mini is the ideal perception configuration, understanding these issues and selectively implementing nodes is challenging. Ensuring the center point functions correctly may be important. Reference
"},{"location":"en/faq.html#awsim","title":"AWSIM","text":""},{"location":"en/faq.html#how-can-i-reset-the-car-to-the-initial-position","title":"How can I reset the car to the initial position?","text":"
Currently, the only way to do this is by restarting AWSIM.
"},{"location":"en/faq.html#awsim-operation-is-unstable","title":"AWSIM operation is unstable.","text":"
This may be due to insufficient GPU performance. If using a high-performance GPU is not feasible, setting the time scale to about 0.5 using the slider at the bottom of the AWSIM screen may stabilize operation.
"},{"location":"en/faq.html#i-want-to-tune-the-mpc-are-the-model-parameters-delay-and-time-constants-used-in-this-awsim-disclosed","title":"I want to tune the MPC. Are the model parameters (delay and time constants) used in this AWSIM disclosed?","text":"
The delay and time constants are neither measured nor disclosed, but the basic specifications are available here.
"},{"location":"en/faq.html#general-competition-questions","title":"General Competition Questions","text":""},{"location":"en/faq.html#is-it-possible-to-add-extra-sensors","title":"Is it possible to add extra sensors?","text":"
To ensure all participants face the same conditions and difficulty, the addition of new sensors is not allowed.
This page outlines the steps to participate in the AI Challenge.
You can participate in this competition with a single PC running Ubuntu 22.04.
First, use the online scoring environment, then proceed with environment setup and development.
"},{"location":"en/getting-started.html#register-for-the-autonomous-driving-ai-challenge-2024","title":"Register for the Autonomous Driving AI Challenge 2024","text":"
Registration for the 2024 competitions have already been closed.
"},{"location":"en/getting-started.html#accessing-and-submitting-to-the-online-scoring-environment","title":"Accessing and Submitting to the Online Scoring Environment","text":"
In this competition, you will upload submission files (compressed source code files) to the online environment, where they will be automatically scored and ranked.
Let's try using the online scoring environment with these four steps!
Info
Accessing the online scoring environment and submitting a file should take about 5 minutes.
After registering for the Autonomous Driving AI Challenge, login information will be sent to your registered email address.
Access the online scoring environment and log in using the credentials provided in the email.
Once you have access, try submitting a source code file. Download the sample code compressed file from the red button below.
Upload the file directly through the \"UPLOAD\" button in the online scoring environment to submit it.
Download the sample code compressed file
"},{"location":"en/getting-started.html#setting-up-the-ai-challenge-environment","title":"Setting Up the AI Challenge Environment","text":"
Please follow the link above to set up the environment.
Info
You can participate in this competition with a single PC running Ubuntu 22.04.
"},{"location":"en/getting-started.html#how-to-proceed-with-development-in-the-ai-challenge","title":"How to Proceed with Development in the AI Challenge","text":"
Let's start developing by following the link above!
"},{"location":"en/getting-started.html#submitting-your-source-code","title":"Submitting Your Source Code","text":"
Submit your completed code via the online scoring environment. Set up your submission using the link above.
This competition is a new initiative aimed at discovering and nurturing engineers who will lead the future automotive industry in the new technological domains known as CASE and MaaS.
The competition involves not only developing programs for autonomous driving mobility but also competing in driving competitions with these developed programs. It aims to provide a platform for engineers, researchers, and students involved in computer science, AI, software, and information processing to challenge themselves, learn, and create organic connections.
"},{"location":"en/index.html#objectives","title":"Objectives","text":""},{"location":"en/index.html#the-role-of-the-competition-from-a-technical-perspective","title":"The Role of the Competition from a Technical Perspective","text":"
Learn SDV (Software Defined Vehicle) development through software integration while understanding hardware
Conduct development using Open Source Software (OSS) as a platform for innovation towards social implementation
"},{"location":"en/index.html#the-role-of-the-competition-in-human-resource-development","title":"The Role of the Competition in Human Resource Development","text":"
Promote participation of engineers from various fields
Accelerate skill development through the provision of educational content
Learn how to develop SDVs by reconciling real machines and simulators
Innovate through digital twin simulations
Create \"aspirations\" and \"passion and excitement\" by combining technical competition with entertainment, using motorsport as a theme
The preliminary round will be conducted through online simulations. The competition aims to achieve faster lap times on the course using AWSIM, which is oriented towards digital twin simulations. Participants will not only learn the structure of Autoware but also adjust parameters for behavior and decision-making parts and develop new algorithms as needed.
The final competition will be conducted using an EV racing kart as the competition vehicle. Participants will apply the knowledge gained from simulations to real vehicles and tackle challenges unique to real vehicles that cannot be replicated in AWSIM.
For example, participants will be challenged to adjust parameters for application to real vehicles and develop algorithms for noise handling and delay countermeasures that cannot be replicated in simulations.
The racing kart will drive around a circuit course and compete for the time it takes to complete a set number of laps. Although the karts will be driving alone this time, in the future they will be driving together with others. Therefore, there is a challenge to avoid virtual objects placed on the course.
For this competition, we have prepared an implementation based on the autonomous driving software Autoware. This page provides background information and explanations on how to utilize this implementation effectively.
In the previous simulation competition, we provided a launch file that could start a reduced configuration of Autoware by limiting functions and reducing the number of nodes from the default Autoware. For the background and purpose of this setup, please refer to the previous competition's documentation.
For this simulation competition, we have similarly prepared a reduced configuration of Autoware designed for use with AWSIM to enable partial use and flexible integration of Autoware.
"},{"location":"en/development/main-module.html#background-of-the-reduced-configuration-of-autoware","title":"Background of the Reduced Configuration of Autoware","text":""},{"location":"en/development/main-module.html#challenges-of-using-autoware","title":"Challenges of Using Autoware","text":"
The default Autoware is composed of many nodes to accommodate various driving environments.
You can also view the configuration diagram of ROS nodes that constitute Autoware in the official Autoware documentation. The current diagram is shown below.
Autoware is equipped with a wide range of functions in each component related to autonomous driving, designed to handle complex driving environments.
However, understanding this complex configuration, the meaning and adjustment of each parameter, and switching or replacing modules is not necessarily easy.
"},{"location":"en/development/main-module.html#preparing-a-reduced-configuration-of-autoware-micro","title":"Preparing a Reduced Configuration of Autoware-Micro","text":"
Therefore, in the previous simulation competition, we prepared a reduced configuration of Autoware by limiting functions and reducing the number of nodes from the default Autoware.
The node diagram of Autoware-Micro is shown below. You can see that the number of nodes has significantly decreased, and only the functions necessary for basic autonomous driving are included.
Features of Autoware-Micro include:
Almost all nodes are started directly from a single launch file.
Parameters are written directly at the node startup, making it easy to track which parameters are needed for which nodes.
The ROS topic names used for input and output of each node are directly remapped at the node startup, making it easy to change the topic names.
By writing autonomous driving software based on this Autoware, you can:
Understand the inner workings of Autoware more deeply due to its simple configuration.
Easily replace Autoware modules with your custom modules to work on functionality improvements.
Clearly see the impact of parameter changes on the overall system operation.
Add existing Autoware nodes that are not included in this version of Autoware.
Changes and features of each component include:
Localization: Self-position estimation using GNSS, IMU, and wheel speed.
Planning: Simplified by omitting behavior_velocity_planner and obstacle_stop_planner, directly outputting a driving trajectory from the output route.
Control: A simple implementation example of control with simple_pure_pursuit.
By utilizing Autoware-Micro, you can focus on the challenges of this competition:
Strategic route planning for curves.
Vehicle control at high speeds.
Moreover, while referring to the implementation example of Autoware-Micro, you can try implementation methods slightly different from Autoware's architecture or create and introduce new custom nodes.
By incorporating your custom nodes, you can improve driving performance and increase your score.
For example, you can consider the following configuration, implement \"Planning\" and \"Control\" separately, or implement a node that handles both \"Planning & Control.\"
You are free to customize as long as the ROS topics for route input and vehicle interface output match.
When there are significant updates to the competition environment, announcements will be made accordingly. For reference, the following instructions are provided.
cd aichallenge2024 # path to aichallenge2024\ngit pull origin/main\n
"},{"location":"en/development/reference.html#installing-awsim-with-visualization","title":"Installing AWSIM with Visualization","text":"
If you want to check the simulation screen of AWSIM, follow the steps in this guide to install AWSIM with visualization.
"},{"location":"en/development/reference.html#setting-up-three-terminals-for-debugging-reference","title":"Setting up Three Terminals for Debugging (Reference)","text":"
To develop with three terminals for debugging, open the first terminal using Alt+Ctrl+T and then execute the following commands by pasting them with Ctrl+Shift+P and pressing Enter.
cd ~/aichallenge-2024\n./docker_run.sh dev cpu\n
cd /aichallenge\nbash run_simulator.bash\n
Open the second terminal using Alt+Ctrl+T and then execute the following commands by pasting them with Ctrl+Shift+P and pressing Enter.
cd ~/aichallenge-2024\n./docker_run.sh dev cpu\n
cd /aichallenge\nbash run_autoware.bash\n
Open the third terminal using Alt+Ctrl+T and then execute the following commands by pasting them with Ctrl+Shift+P and pressing Enter.
cd ~/aichallenge-2024\n./docker_run.sh dev cpu\n
cd /aichallenge\nros2 topic pub --once /control/control_mode_request_topic std_msgs/msg/Bool '{data: true}' >/dev/null\n
When the screen below appears, the startup is complete. To terminate, press CTRL + C in each terminal.
"},{"location":"en/development/workspace-usage.html","title":"How to Proceed with the AI Challenge","text":"
The AI Challenge leverages open-source software. By utilizing the code and web platform provided by the organizers, you can skip the initial development phase and immediately start developing in line with the competition theme. This approach has the significant advantage of avoiding \"reinventing the wheel.\" Additionally, it allows anyone to easily participate in the competition and enables the competition to be run with consistent evaluation criteria.
For first-time participants, you will start from a state where most of the functions necessary for autonomous driving are already in place, standing on the foundation built by your predecessors. From here, you have the opportunity to deepen your unique development in the competition field through the community's \"publication of efforts.\" Furthermore, to deepen your understanding of autonomous driving, we recommend using the \" Autoware Practice \" prepared by the organizers and the learning programs provided by the ROS 2 community, such as \" ROS 2 \".
For those who have already participated in the challenge, we encourage you to share your experiences, contribute to the community, and help the competition evolve. Your active participation will contribute to making the competition even more fulfilling.
The source code that forms the basis for development in the AI Challenge is provided in the competition repository .
Participants will proceed with development by customizing this code and parameters. However, if you are unfamiliar with Autoware, we recommend going through the introductory course first.
For those who want to know the specifications, such as those developing independently without using the repository's code, refer to the interface specifications and simulator specifications pages.
"},{"location":"en/development/workspace-usage.html#read-the-reference-articles-by-voluntary-participants","title":"Read the reference articles by voluntary participants","text":"
The efforts of voluntary participants are summarized in the Advent Calendar , so please refer to them.
If you are unsure where to start, we recommend starting with this article written by Mr. Arata Tanaka, who won the Community Contribution Award in 2023.
"},{"location":"en/development/workspace-usage.html#try-changing-the-parameters","title":"Try changing the parameters","text":"
For those who are unsure what to do after setting up the environment, try adjusting the parameters first. This time, let's change the parameters of the control module simple_pure_pursuit.
Let's adjust the value values below in $HOME/aichallenge-2024/aichallenge/workspace/src/aichallenge_submit/aichallenge_submit_launch/launch/reference.launch.xml.
After customizing the workspace, refer to this to submit.
"},{"location":"en/development/workspace-usage.html#next-step-learn-about-the-main-module","title":"Next Step: Learn about the Main Module","text":""},{"location":"en/information/rules.html","title":"Rules","text":""},{"location":"en/information/rules.html#overview","title":"Overview","text":"
Teams will compete to achieve the shortest driving time while completing the specified number of laps on a designated course.
The course will have a \"Start Area,\" \"Control Line,\" and \"Pit Stop Area.\" Vehicles will start from the Start Area, and the driving time will be measured when they touch the Control Line. For details on the Pit Stop Area, refer to the \"Pit Stop\" section below. Each team will drive individually, without other vehicles or obstacles on the course simultaneously.
Each team will have a preparation session to set up their vehicle and a recording session to measure driving times. However, in the preliminary competition, vehicles will not be used, so there will be no preparation session. Advanced class teams can always perform vehicle maintenance, so they do not have a preparation session either.
Item Final Competition Preliminary Competition Preparation Session TBD None Recording Session TBD 7:00 Number of Laps TBD 6"},{"location":"en/information/rules.html#starting-the-drive","title":"Starting the Drive","text":"
Vehicles will start from the Start Area, and the driving time will begin when they first touch the Control Line. In the preliminary competition, vehicles will be pre-positioned in a predetermined posture. In the final competition, vehicles can be placed in any posture within the Start Area, but operations on the vehicle are only allowed within the Start Area.
"},{"location":"en/information/rules.html#ending-the-drive","title":"Ending the Drive","text":"
The drive will end and be recorded as a result under the following conditions:
The specified number of laps is completed.
The allotted time for the recording session has elapsed.
The vehicle is touched and operated.
Any other reason deemed appropriate by the organizers.
"},{"location":"en/information/rules.html#stopping-the-drive","title":"Stopping the Drive","text":"
The drive will end and be invalidated under the following conditions:
(Preliminary only) The vehicle has not passed the Control Line within 2 minutes from the start of the recording session.
(Preliminary only) The vehicle has significantly deviated from the course.
The course walls are moved.
Any other reason deemed appropriate by the organizers.
Vehicles have a virtual value called \"Condition,\" which, when increased, restricts their speed. Condition increases as the vehicle drives and also when it collides with virtual obstacles described below. The Condition can be reset to its initial value by stopping in the Pit Stop Area for a specified number of seconds.
Setting Item Value Additional Notes Pit Stop Time 3.0 seconds \u2015 Speed Limit Activation 1000 Maximum speed is limited to 20 km/h Section Pass 30 \u2015 Virtual Obstacle Collision 20 - 380 Varies depending on the collision"},{"location":"en/information/rules.html#pit-stop-area","title":"Pit Stop Area","text":"
The Pit Stop Area is indicated by a green frame as shown in the image below.
The course is virtually divided into multiple sections, and Condition increases by a fixed amount each time the vehicle exits a section. Additionally, virtual obstacles displayed with a purple frame, as shown in the image below, are placed on the course, and Condition increases if the vehicle collides with them (virtual obstacles do not affect the physical behavior of the vehicle).
Virtual obstacles are generated at random positions within a section each time the vehicle exits a section. After the first lap, virtual obstacles are removed and regenerated in the section, so multiple virtual obstacles will not be placed within the same section. Also, no virtual obstacles are generated near the Pit Stop Area.
"},{"location":"en/information/schedule.html","title":"Competition Information","text":""},{"location":"en/information/schedule.html#overall-flow","title":"Overall Flow","text":""},{"location":"en/information/schedule.html#schedule","title":"Schedule","text":"Event Date Participant Registration May 27, 2024 - July 1, 2024 Networking Event June 21, 2024 Preliminary Round July 2, 2024 - September 2, 2024 Preliminary Awards Ceremony Around September 2024 (tentative) Practice Day October 10-11, 2024 Practice Day November 1, 2024 Semifinals November 2, 2024 Finals November 3, 2024 Finals Awards Ceremony & Networking Event Around December 2024"},{"location":"en/preliminaries/check-results.html","title":"Checking Results","text":"
This page explains the rules and ranking system for the competition. Please note that the content of this page may change during the competition period.
Scores will be calculated based on the following steps. If multiple runs are made, the higher score will be adopted. If a run is stopped, it will be treated as having completed 0 laps.
The number of laps completed at the end of the run.
The shortest total lap time up to the final lap.
Special Awards: Preliminary rounds will have a seeding system, and finals will have awards.
Best Lap Time: Measured using SIM in the preliminaries and TOM\u2019S system in the finals.
Best Comfortable Ride: Measured using SIM in the preliminaries and by measuring the water in a glass in the finals.
Interaction and recognition of engineers from various fields.
In this competition, scoring will be conducted using an online environment equipped with a simulator and automatic scoring functions. Please follow the steps below to upload your created packages to the online environment. Once uploaded, the simulation will automatically start, and the results will be displayed.
Submit your work by following these steps:
Compress the source code.
Verify the operation in the local evaluation environment.
Submit to the online scoring environment.
"},{"location":"en/preliminaries/submission.html#upload-procedure-to-the-online-environment","title":"Upload Procedure to the Online Environment","text":"
Operation Verification
1.1. Preparation
Compress aichallenge_submit and generate a folder for result output.
Run: ./create_submit_file.bash
1.2. Build Docker Image
Run: ./docker_build.sh eval
1.3. Start Docker Container
After startup, Autoware will automatically launch, and autonomous driving will begin.
For GPU version of AWSIM: ./docker_run.sh eval gpu
For CPU version of AWSIM: ./docker_run.sh eval cpu
1.4. Check result.json
After evaluation is complete, the following files will be stored in the output/latest folder:
autoware.log
rosbag2_autoware
capture
result-summary.json
result-details.json
Upload to the Online Environment
Access the online environment and log in from the \u201cLOG IN\u201d button in the top right corner.
Once logged in, upload aichallenge_submit.tar.gz from the green \u201cUPLOAD\u201d button. After uploading, the source code will be built, and the simulation will be executed in sequence.
If it completes successfully, it will display \"Scoring Completed,\" and you can download result.json, with each lap time displayed.
If the scenario execution fails, such as a launch failure, resulting in no score output, it will display \"No Results.\" In this case, please re-upload, as it might be an internal server error. Contact support if the problem persists.
If the build fails, it will display \"Build Failed.\" Check the steps and re-upload.
The highest score from all previous scorings will be applied to the ranking.
You cannot upload new source code while scoring is in progress.
You can upload up to 10 times per day, with the count reset at midnight Japan time.
Check Results
After evaluation in the online environment, you can download result.json. Download and check the results.
If There Are No Results
4.1. Check for package dependency issues
Verify that there are no missing dependencies in package.xml, setup.py, or CMakeLists.txt, depending on the language used.
4.2. Check Docker
Check inside Docker with the following command to ensure everything is correctly installed and built in the required directories.
Run: docker run -it aichallenge-2024-eval:latest /bin/bash
Verify the following directories:
/aichallenge/workspace/*
/autoware/install/*
"},{"location":"en/setup/build-docker.html","title":"Building and Running the Competition Repository","text":"
In the competition repository, the actual runtime environment is provided entirely within Docker. The steps to use the repository are as follows:
Build the Docker image for the competition environment
Build Autoware within the Docker container
Run Autoware and the simulator simultaneously within the Docker container
"},{"location":"en/setup/build-docker.html#building-the-docker-image-for-the-competition-environment","title":"Building the Docker Image for the Competition Environment","text":"
Open a terminal again using Alt+Ctrl+T. Follow the commands below by pasting them with Ctrl+Shift+P and pressing Enter.
First, navigate to the competition repository.
cd ~/aichallenge-2024\n
Build the Docker image.
./docker_build.sh dev\n
Check if the following image has been generated by running:
docker images\n
You should see an image like this:
aichallenge-2024-dev latest df2e83a20349 33 minutes ago 8.9GB\n
"},{"location":"en/setup/build-docker.html#building-autoware-within-the-docker-container","title":"Building Autoware within the Docker Container","text":"
Start the Docker container by executing the following:
cd ~/aichallenge-2024\n./docker_run.sh dev cpu\n
Although it may not look like anything has changed, the above command moves the environment inside the Docker container. To confirm that you are inside the Docker container, you can run the following command and check if nothing is displayed.
ls ~\n
Within the Docker container, build Autoware by executing:
cd /aichallenge\n./build_autoware.bash\n
"},{"location":"en/setup/build-docker.html#running-autoware-and-the-simulator-within-the-docker-container","title":"Running Autoware and the Simulator within the Docker Container","text":"
After building Autoware, run the following command:
./run_evaluation.bash\n
When the screen shown below appears, the startup is complete. To terminate, press CTRL + C in the terminal.
This concludes the environment setup! Next, let's proceed to actual development.
"},{"location":"en/setup/docker.html","title":"Installing the Virtual Environment","text":""},{"location":"en/setup/docker.html#installing-dependencies","title":"Installing Dependencies","text":"
"},{"location":"en/setup/docker.html#obtaining-the-docker-image-for-the-autoware-environment","title":"Obtaining the Docker Image for the Autoware Environment","text":"
Download the Docker image for the Autoware environment used in the AI Challenge.
The Docker image is approximately 10GB in size, so it is recommended to use a wired LAN for downloading.
For first-time users, proceed to the documentation for headless AWSIM. If you have a PC with a GPU and want a richer development environment, proceed to the documentation for AWSIM with visualization.
Download the latest AWSIM_GPU_**.zip file from Google Drive and extract it to aichallenge-2024/aichallenge/simulator.
Confirm that the executable file exists at aichallenge-2024/aichallenge/simulator/AWSIM/AWSIM.x86_64.
Change the permissions as shown in the diagram.
With this, the environment setup is complete!
"},{"location":"en/setup/headless-simulation.html#next-step-building-and-running-the-competition-repository","title":"Next Step: Building and Running the Competition Repository","text":""},{"location":"en/setup/requirements.html","title":"Recommended Environment","text":"
For the PC used in this competition, we recommend the following specifications. While it is possible to run with lower specifications, it may result in unstable execution speeds on the ROS 2 side, causing significant variations in behavior during simulations.
Warning
If you only have a Windows environment, please install Ubuntu 22.04. While it is possible to install Ubuntu on the same disk as your Windows environment, if you are not familiar with the process, you may accidentally damage your Windows environment. Therefore, we strongly recommend purchasing a new external or internal SSD and installing Ubuntu there.
Info
For guidance on installing Ubuntu, this article may be helpful.
CPU: Intel Core i5 (4 cores) or higher (recommended)
Memory:
Minimum: 8 GB
Recommended: 16 GB or more
SSD: 60 GB or more
"},{"location":"en/setup/requirements.html#using-awsim-with-visualization","title":"Using AWSIM with Visualization","text":"
OS: Ubuntu 22.04
CPU: Intel Core i7 (8 cores) or higher
GPU: NVIDIA GeForce with 8 GB VRAM
Memory: 16 GB or more
Storage: SSD 60 GB or more
"},{"location":"en/setup/requirements.html#next-step-clone-the-workspace","title":"Next Step: Clone the Workspace","text":""},{"location":"en/setup/visible-simulation.html","title":"Downloading AWSIM with Visualization (Reference)","text":"
By default, we distribute a headless version of AWSIM, but we also provide instructions for setting up an environment with visualization for those who wish to use it. Note that setting up a GPU-based environment can often lead to issues, so if you cannot meet the recommended environment specifications or if this is your first time participating, please consider this as a reference.
If you are using AWSIM with visualization, start the container with the following commands:
cd aichallenge-2024\n./docker_build.sh dev\n./docker_run.sh dev gpu\n
Within the terminal where the container is running (inside the container), execute the following:
cd /aichallenge\n./build_autoware.bash\n
After building Autoware, modify run_simulator.bash. Specify the directory you just extracted for AISIM_GPU_**.
#!/bin/bash\n\n# shellcheck disable=SC1091\nsource /aichallenge/workspace/install/setup.bash\nsudo ip link set multicast on lo\n/aichallenge/simulator/AWSIM_GPU_**/AWSIM.x86_64\n
Make the following changes to run_evaluation.bash as well.
"},{"location":"en/setup/workspace-setup.html","title":"Cloning the Workspace","text":""},{"location":"en/setup/workspace-setup.html#installing-dependencies","title":"Installing Dependencies","text":"
Open a terminal with Alt+Ctrl+T, then paste the following commands using Ctrl+Shift+P and press Enter. First, install the necessary libraries.
sudo apt update\nsudo apt install -y git\n
"},{"location":"en/setup/workspace-setup.html#cloning-the-competition-repository","title":"Cloning the Competition Repository","text":"
Clone the workspace repository. Here, we specify the home directory, but you can place it in any directory of your choice.
cd ~\ngit clone https://github.com/AutomotiveAIChallenge/aichallenge-2024.git\n
"},{"location":"en/setup/workspace-setup.html#next-step-installing-the-virtual-environment","title":"Next Step: Installing the Virtual Environment","text":""},{"location":"en/specifications/hardware.html","title":"Hardware","text":""},{"location":"en/specifications/interface.html","title":"Interface","text":""},{"location":"en/specifications/interface.html#list","title":"List","text":"Interface Name Type Service /control/control_mode_requestautoware_auto_vehicle_msgs/srv/ControlModeCommand Publisher /vehicle/status/control_modeautoware_auto_vehicle_msgs/msg/ControlModeReport Subscription /control/command/control_cmdautoware_auto_control_msgs/msg/AckermannControlCommand Subscription /control/command/actuation_cmdtier4_vehicle_msgs/msg/ActuationCommandStamped Publisher /vehicle/status/actuation_statustier4_vehicle_msgs/msg/ActuationStatusStamped Publisher /vehicle/status/velocity_statusautoware_auto_vehicle_msgs/msg/VelocityReport Publisher /vehicle/status/steering_statusautoware_auto_vehicle_msgs/msg/SteeringReport Subscription /control/command/gear_cmdautoware_auto_vehicle_msgs/msg/GearCommand Publisher /vehicle/status/gear_statusautoware_auto_vehicle_msgs/msg/GearReport Publisher /sensing/gnss/pose_with_covariancegeometry_msgs/msg/PoseWithCovarianceStamped Publisher /sensing/imu/imu_rawsensor_msgs/msg/Imu Publisher /aichallenge/objectsstd_msgs/msg/Float64MultiArray Publisher /aichallenge/pitstop/areastd_msgs/msg/Float64MultiArray Publisher /aichallenge/pitstop/conditionstd_msgs/msg/Int32 Publisher /aichallenge/pitstop/statusstd_msgs/msg/Float32"},{"location":"en/specifications/interface.html#controlcommandcontrol_cmd","title":"/control/command/control_cmd","text":"Name Description stamp Message timestamp lateral.stamp Unused lateral.steering_tire_angle Target steering angle lateral.steering_tire_rotation_rate Unused longitudinal.stamp Unused longitudinal.speed Unused longitudinal.acceleration Target acceleration longitudinal.jerk Unused"},{"location":"en/specifications/interface.html#controlcommandactuation_cmd","title":"/control/command/actuation_cmd","text":"Name Description header.stamp Message timestamp header.frame_id Unused actuation.accel_cmd Accel command value (0.0 to 1.0) actuation.brake_cmd Brake command value (0.0 to 1.0) actuation.steer_cmd Tire angle command value (rad)"},{"location":"en/specifications/interface.html#vehiclestatusactuation_status","title":"/vehicle/status/actuation_status","text":"Name Description header.stamp Data acquisition time header.frame_id Unused status.accel_status Accel current value (0.0 to 1.0) status.brake_status Brake current value (0.0 to 1.0) status.steer_status Tire angle current value (rad)"},{"location":"en/specifications/interface.html#vehiclestatusvelocity_status","title":"/vehicle/status/velocity_status","text":"Name Description header.stamp Data acquisition time header.frame_id Frame ID (base_link) longitudinal_velocity Longitudinal velocity lateral_velocity Lateral velocity heading_rate Angular velocity"},{"location":"en/specifications/interface.html#vehiclestatussteering_status","title":"/vehicle/status/steering_status","text":"Name Description stamp Data acquisition time steering_tire_angle Steering angle"},{"location":"en/specifications/interface.html#controlcommandgear_cmd","title":"/control/command/gear_cmd","text":"Name Description stamp Message timestamp command Gear type"},{"location":"en/specifications/interface.html#vehiclestatusgear_status","title":"/vehicle/status/gear_status","text":"Name Description stamp Data acquisition time report Gear type"},{"location":"en/specifications/interface.html#sensinggnsspose_with_covariance","title":"/sensing/gnss/pose_with_covariance","text":"Name Description header.stamp Data acquisition time header.frame_id Frame ID (map) pose.pose.position Vehicle position (origin of base_link) pose.pose.orientation Unused pose.covariance Position accuracy"},{"location":"en/specifications/interface.html#sensingimuimu_raw","title":"/sensing/imu/imu_raw","text":"Name Description header.stamp Data acquisition time header.frame_id Frame ID (imu_link) orientation Orientation angular_velocity Angular velocity linear_acceleration Linear acceleration"},{"location":"en/specifications/interface.html#aichallengeobjects","title":"/aichallenge/objects","text":"Name Description data[N * 4 + 0] X coordinate of Nth object data[N * 4 + 1] Y coordinate of Nth object data[N * 4 + 2] Z coordinate of Nth object data[N * 4 + 3] Radius of Nth object"},{"location":"en/specifications/interface.html#aichallengepitstoparea","title":"/aichallenge/pitstop/area","text":"Name Description data[0] X position of Pit Stop Area data[1] Y position of Pit Stop Area data[2] Z position of Pit Stop Area data[3] X quaternion of Pit Stop Area data[4] Y quaternion of Pit Stop Area data[5] Z quaternion of Pit Stop Area data[6] W quaternion of Pit Stop Area data[7] X size of of Pit Stop Area data[8] Y size of of Pit Stop Area"},{"location":"en/specifications/interface.html#aichallengepitstopcondition","title":"/aichallenge/pitstop/condition","text":"Name Description data Current condition value"},{"location":"en/specifications/interface.html#aichallengepitstopstatus","title":"/aichallenge/pitstop/status","text":"Name Description data Number of seconds a pit stop is valid"},{"location":"en/specifications/simulator.html","title":"Simulator","text":""},{"location":"en/specifications/simulator.html#overview","title":"Overview","text":"
This page describes the specifications of the simulator used in the AI Challenge.
The simulator is based on the open-source autonomous driving simulator \"AWSIM\" developed for Autoware.
"},{"location":"en/specifications/simulator.html#commandline-options","title":"Commandline Options","text":"Option Type Default Description --timeout float 420.0 Set session timeout seconds. --endless bool false Enable/disable session timeout. --pit-stop bool true Enable/disable features related to pit-stop. --replay0 string Load driving logs and replay as a different vehicle.
Use result-details.json for the driving log for replay. Also, replay supports 10 vehicles from --replay0 to --replay9.
"},{"location":"en/specifications/simulator.html#keyboard-operation","title":"Keyboard Operation","text":"Operation Key Quit Esc Reset Space Switch camera C Accel Arrow Up Brake Arrow Down Steering Arrow Left, Right Gear (D) D Gear (R) R Gear (N) N Gear (P) P"},{"location":"en/specifications/simulator.html#topic-operation","title":"Topic Operation","text":"Topic Type Description /aichallenge/awsim/status std_msgs.msg.Float32MultiArray Get status of the simulation. /aichallenge/awsim/change_time_scale std_msgs.msg.Float32 Set the timescale for the simulation. /aichallenge/awsim/reset std_msgs.msg.Empty Reset the simulation.
The above /aichallenge/awsim/status has the following structure.
Index Value 0 session timeout 1 lap count 2 lap time 3 section 4 timescale"},{"location":"en/specifications/simulator.html#vehicle-racing-kart","title":"Vehicle (Racing Kart)","text":"
The vehicle conforms to the specifications of the EGO Vehicle in AWSIM and is designed with specifications close to an actual racing kart.
The following table summarizes the vehicle parameters.
Item Value Vehicle Weight 160 kg Length 200 cm Width 145 cm Front Wheel Diameter 24 cm Front Wheel Width 13 cm Front Wheel Tread 93 cm Rear Wheel Diameter 24 cm Rear Wheel Width 18 cm Rear Wheel Tread 112 cm Maximum Steering Angle 80\u00b0 Maximum Acceleration 3.2 m/s^2"},{"location":"en/specifications/simulator.html#vehicle-component","title":"Vehicle Component","text":"
The following table summarizes the settings of the Vehicle component. For detailed information of the setting items, see this manual.
Item Value Vehicle Settings Use Inertia Off Physics Settings (experimental) Sleep Velocity Threshold 0.02 Sleep Time Threshold 0 Skidding Cancel Rate 0.236 Input Settings Max Steer Angle Input 80 Max Acceleration Input 3.2"},{"location":"en/specifications/simulator.html#rigidbody-component","title":"Rigidbody Component","text":"
The following table summarizes the settings of the Rigidbody component. For more information, see this manual.
Item Value Mass 160 Drag 0 Angular Drag 0"},{"location":"en/specifications/simulator.html#com-position","title":"CoM Position","text":"
CoM (Center of Mass) is the mass center of the vehicle Rigidbody. The CoM position is set at the center of the vehicle and at the height of the wheel axles.
Vehicle collider is used to detect collision between the vehicle and other objects or checkpoints. The vehicle collider is created based on the mesh of the vehicle object.
The vehicle has a total of four wheel colliders - one for each wheel, simulating the vehicle on a four-wheel model, rather than a kinematic bicycle model.
The Wheel Collider is set as follows. For more details on wheel colliders, please refer to this manual.
Item Value Mass 1 Radius 0.12 Wheel Damping Rate 0.25 Suspension Distance 0.001 Suspension Spring Spring (N/m) 35000 Damper (N*s/m) 3500 Target Position 0.01"},{"location":"en/specifications/simulator.html#sensor-configuration","title":"Sensor Configuration","text":""},{"location":"en/specifications/simulator.html#gnss","title":"GNSS","text":"
The GNSS is mounted at the following position relative to the vehicle base link.
Item Value x 0.0 m y 0.0 m z 0.0 m roll 0.0 rad pitch 0.0 rad yaw 0.0 rad"},{"location":"en/specifications/simulator.html#imu","title":"IMU","text":"
The IMU is mounted at the following position relative to the vehicle base link.
Item Value x 0.0 m y 0.0 m z 0.0 m roll 0.0 rad pitch 0.0 rad yaw 0.0 rad"}]}
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