Skip to content

Commit

Permalink
docs: modify description about 2d detector
Browse files Browse the repository at this point in the history 
Signed-off-by: Shin-kyoto <[email protected]>
  • Loading branch information
@Shin-kyoto
Shin-kyoto committed Nov 22, 2023
1 parent 6ff9417 commit 21c9fea
Showing 1 changed file with 13 additions and 13 deletions.
26 changes: 13 additions & 13 deletions docs/design/autoware-architecture/perception/index.md
View file
Original file line number Diff line number Diff line change
Expand Up @@ -78,19 +78,19 @@ As mentioned in the goal session, this perception module is designed to be exten

## Supported Functions

| Feature | Description | Requirements |
| ---------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------- |
| LiDAR DNN based 3D detector | This module takes point clouds as input and performs detection of objects such as vehicles, trucks, buses, pedestrians, and bicycles. | - Point Clouds |
| Camera DNN based 2D detector | This module takes camera image as input and performs detection of objects such as vehicles, trucks, buses, pedestrians, and bicycles. | - Camera Images |
| LiDAR Clustering | This module performs clustering of point clouds and shape estimation to achieve object detection without labels. | - Point Clouds |
| Semi-rule based detector | This module performs object detection using information from both images and point clouds, and it consists of two components: LiDAR Clustering and Camera DNN based 2D detector. | - Output from Camera DNN based 2D detector and LiDAR Clustering |
| Object Merger | This module integrates results from various detectors. | - Detected Objects |
| Interpolator | This module stabilizes the object detection results by maintaining long-term detection results using Tracking results. | - Detected Objects <br> - Tracked Objects |
| Tracking | This module gives ID and estimate velocity to the detection results. | - Detected Objects |
| Prediction | This module predicts the future paths (and their probabilities) of dynamic objects according to the shape of the map and the surrounding environment. | - Tracked Objects <br> - Vector Map |
| Obstacle Segmentation | This module identifies point clouds originating from obstacles that the ego vehicle should avoid. | - Point Clouds <br> - Point Cloud Map |
| Occupancy Grid Map | This module detects blind spots (areas where no information is available and where dynamic objects may jump out). | - Point Clouds <br> - Point Cloud Map |
| Traffic Light Recognition | This module detects the position and state of traffic signals. | - Camera Images <br> - Vector Map |
| Feature | Description | Requirements |
| ---------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------- |
| LiDAR DNN based 3D detector | This module takes point clouds as input and detects objects such as vehicles, trucks, buses, pedestrians, and bicycles. | - Point Clouds |
| Camera DNN based 2D detector | This module takes camera images as input and detects objects such as vehicles, trucks, buses, pedestrians, and bicycles in the two-dimensional image space. It detects objects within image coordinates and providing 3D coordinate information is not mandatory. | - Camera Images |
| LiDAR Clustering | This module performs clustering of point clouds and shape estimation to achieve object detection without labels. | - Point Clouds |
| Semi-rule based detector | This module detects objects using information from both images and point clouds, and it consists of two components: LiDAR Clustering and Camera DNN based 2D detector. | - Output from Camera DNN based 2D detector and LiDAR Clustering |
| Object Merger | This module integrates results from various detectors. | - Detected Objects |
| Interpolator | This module stabilizes the object detection results by maintaining long-term detection results using Tracking results. | - Detected Objects <br> - Tracked Objects |
| Tracking | This module gives ID and estimate velocity to the detection results. | - Detected Objects |
| Prediction | This module predicts the future paths (and their probabilities) of dynamic objects according to the shape of the map and the surrounding environment. | - Tracked Objects <br> - Vector Map |
| Obstacle Segmentation | This module identifies point clouds originating from obstacles that the ego vehicle should avoid. | - Point Clouds <br> - Point Cloud Map |
| Occupancy Grid Map | This module detects blind spots (areas where no information is available and where dynamic objects may jump out). | - Point Clouds <br> - Point Cloud Map |
| Traffic Light Recognition | This module detects the position and state of traffic signals. | - Camera Images <br> - Vector Map |

## Reference Implementation

Expand Down

0 comments on commit 21c9fea

Please sign in to comment.