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

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 raci...

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Bibliographic Details
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
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Summary: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.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2023.3295252