  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.
