Automated Parking Assist (APA) systems are now facing great challenges of 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.

翻译：自动泊车辅助系统（APA）因用户对泊车能力、可靠性及完成效率的担忧，目前在应用中的采纳率面临巨大挑战。为升级传统APA规划器并提升用户接受度，本研究提出了一种基于最优控制的停车运动规划器。其核心亮点在于控制逻辑：通过镜像停车目标进行轨迹规划。该方法能够实现：i）在狭窄空间中的泊车能力；ii）通过扩展运行设计域（ODD）提升泊车可靠性；iii）加快泊车过程完成速度；iv）增强计算效率；v）适用于所有泊车类型。通过全面评估，结果表明所提规划器将泊车成功率提升了40.6%，泊车完成效率提高了18.0%，ODD扩展了86.1%。在平行泊车场景及狭窄空间等困难泊车案例中展现出优越性。此外，该规划器的平均计算时间为74毫秒。结果表明所提规划器已具备实时商业应用条件。