Mirroring the Parking Target: An Optimal-Control-Based Parking Motion Planner With Strengthened Parking Reliability and Faster Parking Completion

2024-05-13

IEEE transactions on intelligent transportation systems (Print)

AI Summary

Low user acceptance of Automatic Parking Assist (APA) systems stems from poor reliability, low efficiency, and narrow operational design domain (ODD) coverage. To address these limitations, this paper proposes an optimal-control-based trajectory planning method employing a mirror-target-point strategy. By innovatively leveraging geometric mirror transformation, the approach generates feasible parking trajectories that uniformly support parallel, perpendicular, and angle parking configurations—significantly expanding the ODD. The method integrates real-time trajectory optimization with rigorous ODD modeling and evaluation, ensuring both adaptability in confined spaces and automotive-grade computational efficiency. Experimental validation demonstrates a 40.6% improvement in parking success rate, an 18.0% increase in task completion efficiency, an 86.1% expansion of ODD coverage, and an average computation time of only 74 ms per planning instance.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

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.

Problem

• Enhance parking reliability and success rate
• Improve parking completion efficiency and speed
• Expand operational domain for narrow spaces

Innovation

• Optimal-control-based parking motion planner
• Mirroring parking target for trajectory planning
• Enhances reliability, efficiency, and computational speed