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.

• x86 CPU (8 or more cores)
• 16 GB or more of memory
• Nvidia GPU (4GB or more of memory) :

• Ubuntu 18.04
• Nvidia driver

If cuda or tensorRT is already installed, it is recommended to remove it.

1. Clone this repository

git clone https://github.com/tier4/AutowareArchitectureProposal.git
cd AutowareArchitectureProposal/

2. Run the setup script

./setup_ubuntu18.04.sh

In this step, the following software are installed. Please confirm their licenses before using them.

• osqp
• ROS Melodic
• CUDA 10.2
• cuDNN 7
• TensorRT 7
• geographiclib-tools

3. 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.

1. Download sample map from here and extract the zip file.

2. Download sample rosbag from here.

3. Launch Autoware

source devel/setup.bash
roslaunch autoware_launch autoware.launch map_path:=[path] rosbag:=true

4. Play rosbag

rosbag play --clock [rosbag file] -r 0.2

• 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

1. Download sample map from here and extract the zip file.

2. Launch Autoware

source devel/setup.bash
roslaunch autoware_launch planning_simulator.launch map_path:=[path]

3. Set initial pose
4. Set goal pose
5. Push engage button. autoware_web_controller

• sample map : © 2020 TierIV inc.

See here. for more information.

• 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

• Neural Network Weight Files