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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    Title :

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


    Contributors:
    Li, Junxiang (author) / Wu, Tao (author) / Zhao, Huijing (author) / Xu, Xin (author)


    Publication date :

    2025-08-01


    Size :

    4550282 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Motion Planning of Intelligent Vehicles: A Survey

    Chen, Yang / He, Hangen / An, Xiangjing | IEEE | 2006


    MACHINE LEARNING BASED MOTION PLANNING APPROACH FOR INTELLIGENT VEHICLES

    Artuñedo, Antonio / Corrales, Gabriel / Villagra, Jorge et al. | British Library Conference Proceedings | 2020


    Machine learning based motion planning approach for intelligent vehicles

    Artunedo, Antonio / Corrales, Gabriel / Villagra, Jorge et al. | IEEE | 2020


    Machine learning based motion planning approach for intelligent vehicles

    Artuñedo, Antonio / Corrales, Gabriel / Villagra, Jorge et al. | BASE | 2020

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