The Autoware Foundation is planning a significant hackathon for Autoware.Auto in April, 2020. This post contains the details.
This post will be amended as more information becomes available.
The goal of the hackathon is to polish off the Autonomous Valet Parking use case functionality and finalise getting it working on a real vehicle.
Because it is not feasible to be implementing self-driving functionality within four days, the intention is to have the necessary functionality in Autoware.Auto completed before the hackathon starts. A simulation of the demonstration location in the LGSVL simulator will be used for testing and development by most developers prior to the hackathon.
Schedule and location
The hackathon will take place over five days in April, 2020. The first four days will be dedicated to finalising the demonstration and fixing any remaining issues in Autoware.Auto. The final day will be the demonstration itself.
The hackathon will be held at Apex.AI’s office in Palo Alto, California. The demonstration itself being held at the AutonomouStuff parking lot, which is about ten minutes’ (manual) drive away.
- The Velodyne driver will be the one developed originally for Autoware.Auto by Apex.AI and being pushed to the
ros-driversrepository for ROS 2.
- Localisation will be performed using the deterministic NDT implementation currently being proposed for Autoware.Auto by Apex.AI.
- Obstacle detection will be based on the euclidean clustering implementation present in Autoware.Auto.
- Planning will use a new behaviour-tree-based planning architecture provided by Tier IV.
- Motion control will use a deterministic implementation of MPC provided by Christopher Ho and Apex.AI.
- The vehicle interface will be provided by AutonomouStuff, who have just recently completed a ROS 2 version of their PACMod driver and considering a port of their Speed and Steering Control software stack.
- The map format used will be OpenDrive.
- Safety will be achieved via an emergency stop, due to the low speed.
The hackathon and demonstration will be performed using Apex.AI’s Lexus. This SUV has two Velodyne 16s mounted front and back for localisation and obstacle detection, an advanced GPS/IMU sensor, and a Nuvo computer.