Path Planning and Tracking Control for Parking via Soft Actor-Critic Under Non-Ideal ScenariosOACSTPCD

Parking in a small parking lot within limited space poses a difficult task.It often leads to deviations between the final parking posture and the target posture.These deviations can lead to partial occupancy of adjacent parking lots,which poses a safety threat to vehicles parked in these parking lots.However,previous studies have not addressed this issue.In this paper,we aim to evaluate the impact of parking deviation of existing vehicles next to the target parking lot(PDEVNTPL)on the automatic ego vehicle(AEV)parking,in terms of safety,comfort,accuracy,and efficiency of parking.A segmented parking training framework(SPTF)based on soft actor-critic(SAC)is proposed to improve parking performance.In the proposed method,the SAC algo-rithm incorporates strategy entropy into the objective function,to enable the AEV to learn parking strategies based on a more com-prehensive understanding of the environment.Additionally,the SPTF simplifies complex parking tasks to maintain the high per-formance of deep reinforcement learning(DRL).The experimen-tal results reveal that the PDEVNTPL has a detrimental influ-ence on the AEV parking in terms of safety,accuracy,and com-fort,leading to reductions of more than 27％,54％,and 26％respectively.However,the SAC-based SPTF effectively mitigates this impact,resulting in a considerable increase in the parking success rate from 71％to 93％.Furthermore,the heading angle deviation is significantly reduced from 2.25 degrees to 0.43 degrees.