Automated parking stands as a cornerstone technology for autonomous vehicles operating in low-speed scenarios and has been extensively integrated into various vehicle models. Motion planning represents a pivotal technical module that generates planned trajectories to ensure vehicle movement from its initial pose to the target pose without collisions while aligning with kinematics rules, epitomizing the intelligence level of automated parking. In the context of ongoing transitions of automated driving technology from preliminary prototype research to industrial applications, automated parking technology in the realm of low-speed autonomous driving emerges as a critical area primed for breakthroughs. This paper offers a thorough review of the motion planning methods applied within the automated parking domain, categorizing them into geometric methods, sampling methods, search methods, numerical optimization methods, and machine learning methods. This paper also retrospectively examines their development and characteristics. Furthermore, this paper delves into the future directions of motion planning research for automated parking, with a particular focus on constructing more generalized models, enhancing the stability of planning methods, emphasizing the capabilities for online real-time high-quality re-planning and defensive driving, and addressing interactions with other traffic participants.


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    Title :

    Motion Planning Methods for Automated Parking: A Comprehensive Review


    Contributors:
    CHEN Xiaoming (author) / LI Bai (author) / FAN Lili (author) / ZHANG Youmin (author)


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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