Mirroring the Parking Target: An Optimal-Control-Based Parking Motion Planner With Strengthened Parking Reliability and Faster Parking Completionoa mark
Automated Parking Assist (APA) systems are now facing great challenges with low adoption in applications, due to users' concerns about parking capability, reliability, and completion efficiency. To upgrade the conventional APA planners and enhance user's acceptance, this research proposes an optimal-control-based parking motion planner. Its highlight lies in its control logic: planning trajectories by mirroring the parking target. This method enables: i) parking capability in narrow spaces; ii) better parking reliability by expanding Operation Design Domain (ODD); iii) faster completion of parking process; iv) enhanced computational efficiency; v) universal to all types of parking. A comprehensive evaluation is conducted. Results demonstrate the proposed planner does enhance parking success rate by 40.6%, improve parking completion efficiency by 18.0%, and expand ODD by 86.1%. It shows its superiority in difficult parking cases, such as the parallel parking scenario and narrow spaces. Moreover, the average computation time of the proposed planner is 74 milliseconds. Results indicate that the proposed planner is ready for real-time commercial applications.
This work was supported in part by the National Science and Technology Major Project under Grant 2022ZD0115501, in part by the National Natural Science Foundation of China under Grant 52302412 and Grant 52372317, in part by the Yangtze River Delta Science and Technology Innovation Joint Force under Grant 2023CSJGG0800, in part by Shanghai Automotive Industry Science and Technology Development Foundation under Grant 2404, in part by the Xiaomi Young Talents Program, in part by the Fundamental Research Funds for the Central Universities, in part by Tongji Zhongte Chair Professor Foundation under Grant 000000375-2018082, in part by the Science Fund of State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle under Grant 32215011, in part by the Postdoctoral Fellowship Program (Grade B) of China Postdoctoral Science Foundation under Grant GZB20230519, in part by Shanghai Sailing Program under Grant 23YF1449600, in part by Shanghai Post-Doctoral Excellence Program under Grant 2022571, and in part by China Postdoctoral Science Foundation under Grant 2022M722405.Manuscript received 13 July 2023; revised 13 June 2024; accepted 8 July 2024. Date of publication 25 July 2024; date of current version 1 November 2024. This work was supported in part by the National Science and Technology Major Project under Grant 2022ZD0115501, in part by the National Natural Science Foundation of China under Grant 52302412 and Grant 52372317, in part by the Yangtze River Delta Science and Technology Innovation Joint Force under Grant 2023CSJGG0800, in part by Shanghai Automotive Industry Science and Technology Development Foundation under Grant 2404, in part by the Xiaomi Young Talents Program, in part by the Fundamental Research Funds for the Central Universities, in part by Tongji Zhongte Chair Professor Foundation under Grant 000000375-2018082, in part by the Science Fund of State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle under Grant 32215011, in part by the Postdoctoral Fellowship Program (Grade B) of China Postdoctoral Science Foundation under Grant GZB20230519, in part by Shanghai Sailing Program under Grant 23YF1449600, in part by Shanghai Post-Doctoral Excellence Program under Grant 2022571, and in part by China Postdoctoral Science Foundation under Grant 2022M722405. The Associate Editor for this article was C. F. Mecklenbr\u00E4uker. (Corresponding author: Haoran Wang.) Jia Hu, Yongwei Feng, and Shuoyuan Li are with the Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China (e-mail: hujia@tongji.edu.cn; 2210176@tongji.edu.cn; 2131304@tongji.edu.cn).
