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.

翻译：经典自主赛车方法利用实时定位跟踪预计算的最优轨迹。相比之下，端到端深度强化学习（DRL）能够训练智能体仅使用原始激光雷达扫描数据进行竞速。当经典方法优先考虑高性能赛车的优化时，DRL方法一直聚焦于低性能应用场景，对速度剖面的考量甚少。本研究致力于解决利用端到端DRL智能体实现高速自主赛车的问题，提出轨迹辅助学习（TAL）方法，通过将最优轨迹（赛车线）融入学习框架来训练高性能DRL智能体。我们采用TD3算法在开源F1Tenth模拟器的四个地图上评估该方法。结果表明，与基准相比，我们的方法在高速条件下实现了显著更高的圈完成率。这得益于TAL训练智能体选择可行的速度剖面——在弯道减速并大致跟踪最优轨迹。