Conflict-constrained Multi-agent Reinforcement Learning Method for Parking Trajectory Planning

Automated Valet Parking (AVP) has been exten-sively researched as an important application of autonomous driving. Considering the high dynamics and density of real parking lots, a system that considers multiple vehicles simultaneously is more robust and efficient than a single vehicle setting as in most studies. In this paper, we propose a dis-tributed Multi-agent Reinforcement Learning(MARL) method for coordinating multiple vehicles in the framework of an AVP system. This method utilizes traditional trajectory planning to accelerate the learning process and introduces collision conflict constraints for policy optimization to mitigate the path conflict problem. In contrast to other centralized multi-agent path finding methods, the proposed approach is scalable, distributed, and adapts to dynamic stochastic scenarios. We train the models in random scenarios and validate in several artificially designed complex parking scenarios where vehicles are always disturbed by dynamic and static obstacles. Experimental results show that our approach mitigates path conflicts and excels in terms of success rate and efficiency.