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

arXiv: https://arxiv.org/abs/2306.07003 · HTML: https://mlsanity.com/paper/2306.07003/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2023.

## Built on by (most cited first, 10 of 10)

- [KiGRAS: Kinematic-Driven Generative Model for Realistic Agent Simulation](https://mlsanity.com/paper/2407.12940/) (2024)
- [Autonomous Driving Small-Scale Cars: A Survey of Recent Development](https://mlsanity.com/paper/2404.06229/) (2024)
- [Drive Fast, Learn Faster: On-Board RL for High Performance Autonomous Racing](https://mlsanity.com/paper/2505.07321/) (2025)
- [Trajectory Planning Using Reinforcement Learning for Interactive Overtaking Maneuvers in Autonomous Racing Scenarios](https://mlsanity.com/paper/2404.10658/) (2024)
- [Physics-Informed Reinforcement Learning of Spatial Density Velocity Potentials for Map-Free Racing](https://mlsanity.com/paper/2604.09499/) (2026)
- [F1tenth Autonomous Racing With Offline Reinforcement Learning Methods](https://mlsanity.com/paper/2408.04198/) (2024)
- [MIND-Stack: Modular, Interpretable, End-to-End Differentiability for Autonomous Navigation](https://mlsanity.com/paper/2505.21734/) (2025)
- [Context-Aware Model-Based Reinforcement Learning for Autonomous Racing](https://mlsanity.com/paper/2510.11501/) (2025)
- [Expert Knowledge-driven Reinforcement Learning for Autonomous Racing via Trajectory Guidance and Dynamics Constraints](https://mlsanity.com/paper/2603.05842/) (2026)
- [Autonomous overtaking trajectory optimization using reinforcement learning and opponent pose estimation](https://mlsanity.com/paper/2603.27207/) (2026)

## Abstract (arXiv)

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.