Jingyu Liu

Autonomous race driving has emerged as a challenging domain for reinforcement learning (RL) applications, requiring high-speed control while adhering to strict safety constraints. Existing RL-based racing methods often struggle to balance performance and safety, with limited adaptability in dynamic racing scenarios with multiple opponent vehicles. The high-dimensional state space and strict safety constraints pose significant challenges for efficient learning. To address these challenges, this paper proposes an integrated RL framework that combines three novel components: (1) a state mapping mechanism that dynamically transforms raw track observations into a consistent representation space; (2) an action mapping technique that rigorously enforces physical traction constraints; and (3) a safe exploration guidance method that combines conservative controllers with RL policies, significantly reducing off-track incidents during training. Extensive experiments conducted in our simulation environment with four test tracks demonstrate the effectiveness of our approach. In time trial scenarios, our method improves lap times by 12–26% and increases the training completion rate from 33.1% to 78.7%. In competitive racing, it achieves a 46–51% higher average speed compared to baseline methods. These results validate the framework’s ability to achieve both high performance and safety in autonomous racing tasks.