Multi-Vehicle Collaborative Trajectory Planning for AVP in Parking Lots: A Bio-Inspired Evolutionary Reinforcement Learning Approach

Efficient trajectory planning in Autonomous Valet Parking (AVP) remains challenging due to multiple vehicle interactions and environmental complexities. Existing single-agent Reinforcement Learning (RL) approaches face challenges in balancing complexity, convergence, and knowledge efficiency, often resulting in increased collisions and longer travel times. To address these issues, this paper proposes a Bio-inspired Evolutionary Reinforcement Learning (BERL) framework for multi-vehicle collaborative trajectory planning, where each vehicle is modeled as a Fusion Architecture for Learning and Cognition Network (FALCON) agent based on Adaptive Resonance Theory (ART). The BERL framework comprises three core modules: 1) Meme Reinforcement Learning (MRL), which enables agents to learn independently and adapt to changing environments; 2) Expert-Guided Evolutionary Learning (EGEL), which facilitates knowledge transfer from expert agents to less experienced ones, enhancing coordination; and 3) Integrated Forgetting and Memory Optimization (IFMO), which optimizes memory use and reduces algorithm complexity. Additionally, the BERL framework supports model and sensor quality heterogeneity in the multi-vehicle trajectory planning scenario. Finally, we build an AVP Simulation (AVPS) platform to validate the performance of the proposed framework. Comprehensive simulation results demonstrate that the BERL framework improves success rate and parking efficiency by at least 15.7% and 16.7%, respectively, as compared to state-of-the-art algorithms. Additionally, the proposed IFMO module reduces the number of memes in the FALCON agent by 30.2% while maintaining stable performance.