High-speed Autonomous Racing using Trajectory-aided Deep Reinforcement Learning

• Published in: IEEE robotics and automation letters 2023-09, Vol.8 (9), p.1-7
• Main Authors: Evans, Benjamin David, Engelbrecht, Herman Arnold, Jordaan, Hendrik Willem
• Format: Article
• Language: English
• Subjects: Accidents; Algorithms; Deep learning; Deep Learning Methods; High speed rail; Laser radar; Machine Learning for Robot Control; Racing; Radar tracking; Reinforcement learning; Sensors; Trajectory; Trajectory optimization
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2023.3295252

The classical method of autonomous racing uses real-time localisation to follow a precalculated optimal trajectory. In contrast, end-to-end deep reinforcement learning (DRL) can train agents to race using only raw LiDAR scans. While classical methods prioritise optimization for high-performance racing, DRL approaches have focused on low-performance contexts with little consideration of the speed profile. This work addresses the problem of using end-to-end DRL agents for high-speed autonomous racing. We present trajectory-aided learning (TAL) that trains DRL agents for high-performance racing by incorporating the optimal trajectory (racing line) into the learning formulation. Our method is evaluated using the TD3 algorithm on four maps in the open-source F1Tenth simulator. The results demonstrate that our method achieves a significantly higher lap completion rate at high speeds compared to the baseline. This is due to TAL training the agent to select a feasible speed profile of slowing down in the corners and roughly tracking the optimal trajectory.