Human-Like Reverse Parking using Deep Reinforcement Learning with Attention Mechanism

This study explores efficient and safe Automated Valet Parking (AVP) strategies in unstructured and dynamic environments. Existing approaches utilizing reinforcement learning neglected the interaction between dynamic agents and ego vehicle, and disregarded human driving patterns, leading to their ineffectiveness in unstructured dynamic environments. We propose a novel hybrid attention mechanism that comprehends the mixed interactions between static and dynamic elements, aiding autonomous vehicles in advanced planning. We implemented a guidance system based on human preferences, eliminating the need for expert data and expediting the training process via intermediate planning stages, thereby facilitating parking maneuvers akin to human drivers. The model was trained and validated in a range of parking situations. The experimental outcomes indicate that our method possesses robust adaptability and navigation skills in static and dynamic environments.