The perception module incorporates the capability of using multiple cameras, radars (front and rear) and LiDARs to recognize obstacles and fuse their individual tracks to obtain a final track list. The obstacle sub-module detects, classifies and tracks obstacles. This sub-module also predicts obstacle motion and position information (e.g., heading and velocity). For lane line, we construct lane instances by postprocessing lane parsing pixels and calculate the lane relative location to the ego-vehicle (L0, L1, R0, R1, etc.).
| Record name | Verification | Vehicle configuration | Map | Size | Download address |
|---|---|---|---|---|---|
| bev_test.tar.xz | bev_detection | Apollo Perception Test Bev | default | 1.93GB | https://apollo-pkg-beta.cdn.bcebos.com/perception_record/bev_test.tar.xz |
| sensor_rgb.tar.xz | camera_detection、lidar_detection、trafficlight_detection | Perception Test V1 | Sunnyvale Loop | 4.4GB | https://apollo-system.bj.bcebos.com/dataset/6.0_edu/sensor_rgb.tar.xz |
Download and install the model in the remote model warehouse to the local through the model deployment tool, for example, download and install PETR_V1_paddle
python modules/tools/amodel/amodel.py install https://apollo-pkg-beta.cdn.bcebos.com/perception_model/petrv1.zipDownload record verification model PETR_V1_paddle
wget https://apollo-pkg-beta.cdn.bcebos.com/perception_record/bev_test.tar.xzUnzip the downloaded record
tar -xvf bev_test.tar.xzNote: bev_test.tar.xz is only used to test the bev algorithm, it's recommended to download data on nuScenes and make it into a record
The general architecture of the perception module is shown:
The detailed perception modules are displayed below.
The perception module inputs are:
- 128 channel LiDAR data (cyber channel /apollo/sensor/velodyne128)
- 16 channel LiDAR data (cyber channel /apollo/sensor/lidar_front, lidar_rear_left, lidar_rear_right)
- Radar data (cyber channel /apollo/sensor/radar_front, radar_rear)
- Image data (cyber channel /apollo/sensor/camera/front_6mm, front_12mm)
- Extrinsic parameters of radar sensor calibration (from YAML files)
- Extrinsic and Intrinsic parameters of front camera calibration (from YAML files)
- Velocity and Angular Velocity of host vehicle (cyber channel /apollo/localization/pose)
The perception module outputs are:
- The 3D obstacle tracks with the heading, velocity and classification information (cyber channel /apollo/perception/obstacles)
- The output of traffic light detection and recognition (cyber channel /apollo/perception/traffic_light)
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Nvidia GPU and CUDA are required to run the perception module with Caffe. Apollo provides the CUDA and Caffe libraries in the release docker image. However, the Nvidia GPU driver is not installed in the dev docker image.
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To run the perception module with CUDA acceleration, install the exact same version of the Nvidia driver in the docker image that is installed on your host machine, and then build Apollo with the GPU option (i.e., using
./apollo.sh build_opt_gpu). -
This module contains a redistribution in binary form of a modified version of caffe. A copy of the caffe's original copyright statement is included below:
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