Interactive Motion Planning Using Genetic-Enhanced Reinforcement Learning with Backup Strategy

Navigating dense traffic environments, particularly during unprotected left-turn maneuvers, poses a critical challenge for autonomous vehicles due to the uncertainty of traffic participants, the trade-off between safety and efficiency, and dynamic interaction between vehicles. Existing methods usually fail to meet the constraints in changing environments or result in overly cautious behavior. This paper proposes a Genetic-Enhanced Reinforcement Learning with Backup Strategy (GERL-BS) to address these challenges by effectively navigating unprotected left turns in dense traffic. By leveraging the evolutionary capabilities of a risk-concerned Genetic Algorithm (GA), GERL-BS enhances the exploration of safety-critical states by distilling essential interaction knowledge and control strategies, overcoming the limitations of Reinforcement Learning (RL) in such high-risk scenarios. Additionally, the Safety-Enhanced Contingency Backup (SECB) Module introduces a reward augmentation mechanism to ensure safety in complex and uncertain environments. To address the diversity and variability in vehicle interactions, we introduced the Heterogeneous Intelligent Driver Model (H-IDM), designed to simulate the heterogeneous behaviors of neighboring vehicles. Comprehensive evaluations in dense traffic scenarios with varying vehicle configurations demonstrate the proposed GERL-BS controller's superior safety, adaptability, and effectiveness.