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

作　　者：Xiaolin Tang Yuyou Yang Teng Liu Xianke Lin Kai Yang Shen Li 

机构地区：[1]IEEE [2]the College of Mechanical and Vehicle Engineering,Chongqing University,Chongqing 400044,China [3]the Clinical Research Center and Medical Pathology Center,Chongqing University Three Gorges Hospital,Chongqing University,Wanzhou 404000 [4]the Department of Automotive and Mechatronics Engineering,Ontario Tech University,Oshawa ON LIG OC5,Canada [5]the School of Civil Engineering,Tsinghua University,Beijing 100084,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2024年第1期181-195,共15页自动化学报（英文版）

基　　金：supported by National Natural Science Foundation of China(52222215, 52272420, 52072051)。

摘　　要：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 algorithm incorporates strategy entropy into the objective function, to enable the AEV to learn parking strategies based on a more comprehensive understanding of the environment. Additionally, the SPTF simplifies complex parking tasks to maintain the high performance of deep reinforcement learning(DRL). The experimental results reveal that the PDEVNTPL has a detrimental influence on the AEV parking in terms of safety, accuracy, and comfort, 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 0.43degrees.

关 键 词：Automatic parking control strategy parking deviation(APS) soft actor-critic(SAC) 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] U491.7[自动化与计算机技术—控制科学与工程]