With the advancement of deep learning, the learning-based motion planning (MP) approach exhibits immense potential in intelligent vehicles (IVs). Because the principle and framework of the learning-based MP method differ from the traditional MP methods, exploring effective strategies to enhance interpretability plays an important role. This survey fills the gaps in the IV field’s learning-based motion planning and interpretability enhancement. Our study aims to explore two fundamental inquiries. Firstly, how can we design learning-based MP to achieve high performance? Secondly, how can we enhance the interpretability of learning-based MP? To this end, this paper provides an extensive overview of more than 200 papers employed in learning-based MP techniques within the last 10 years. By summarizing these techniques, a taxonomy for integrating learning-based MP techniques into an IV architecture is presented as three modes: learning-based key-module generator, learning-based trajectory generator, and learning-based policy generator. Interpretability enhancement has different considerations for different modes. Additionally, we compile a summary of resources utilized in learning-based MP. Finally, we discuss critical challenges and make suggestions.


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    Titel :

    How to Enhance the Interpretability of Learning-Based Motion Planning for Intelligent Vehicles—A Survey


    Beteiligte:
    Li, Junxiang (Autor:in) / Wu, Tao (Autor:in) / Zhao, Huijing (Autor:in) / Xu, Xin (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.08.2025


    Format / Umfang :

    4550282 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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