Autoware (Architecture Proposal)
What's this
This is the source code of the feasibility study for Autoware architecture proposal.
WARNING: This source is solely for demonstrating an architecture proposal. It should not be used to drive cars.
Architecture overview is here.
How to setup
Requirements
Hardware
- x86 CPU (8 or more cores)
- 16 GB or more of memory
- Nvidia GPU (4GB or more of memory) :
Software
- Ubuntu 18.04
- Nvidia driver
If cuda or tensorRT is already installed, it is recommended to remove it.
Autoware setup
- Clone this repository
git clone https://github.com/tier4/AutowareArchitectureProposal.git
cd AutowareArchitectureProposal/
- Run the setup script
./setup_ubuntu18.04.sh
In this step, the following software are installed. Please confirm their licenses before using them.
- Build the source
catkin build --cmake-args -DCMAKE_BUILD_TYPE=Release
Note that the computer need to be connected to Internet to download neural network weight files.
How to run
Simulator
Quick Start
Rosbag
-
Download sample map from here and extract the zip file.
-
Download sample rosbag from here.
-
Launch Autoware
source devel/setup.bash
roslaunch autoware_launch autoware.launch map_path:=[path] rosbag:=true
- Play rosbag
rosbag play --clock [rosbag file] -r 0.2
Note
- sample map : © 2020 TierIV inc.
- rosbag : © 2020 TierIV inc.
- Image data are removed due to privacy concerns.
- Cannot run traffic light recognition
- Decreased accuracy of object detection
- Image data are removed due to privacy concerns.
Planning Simulator
-
Download sample map from here and extract the zip file.
-
Launch Autoware
source devel/setup.bash
roslaunch autoware_launch planning_simulator.launch map_path:=[path]
- Set initial pose
- Set goal pose
- Push engage button. autoware_web_controller
Note
- sample map : © 2020 TierIV inc.
Tutorial in detail
See here. for more information.
References
Videos
- Scenario demo
- Obstacle avoidance in the same lane
- Obstacle avoidance by lane change
- Object recognition
- Auto parking
- 360° FOV perception(Camera Lidar Fuison)
- Robustness of localization