Decision-making for autonomous vehicles is critical to achieving safe and efficient autonomous driving. In recent years, deep reinforcement learning (DRL) techniques have emerged as the most promising way to enable intelligent decision-making. However, DRL with ‘black box’ nature is not widely understood by humans, thus hindering their social acceptance. In this paper, we combine SHapley Additive exPlanation (SHAP) and random forest (RF) techniques to bring transparency to decision-making obtained by DRL. Specifically, we first implement decision-making of autonomous vehicles following in discrete action space based on DRL algorithm with the goal of safety and efficiency. Then we use the SHAP technique to simplify the feature space, which shows that relative distance, longitudinal speed of the ego vehicle, and longitudinal speed of the proceeding vehicle have a critical impact on vehicle following task. Finally, we collect the state-action pairs generated by the DRL model and perform feature filtering, and fit the decision model with an interpretable RF model. The simulation results show that the RF model achieves the behavioral explanation of autonomous vehicle following.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    An interpretation framework for autonomous vehicles decision-making via SHAP and RF


    Beteiligte:
    Cui, Zhihao (Autor:in) / Li, Meng (Autor:in) / Huang, Yanjun (Autor:in) / Wang, Yulei (Autor:in) / Chen, Hong (Autor:in)


    Erscheinungsdatum :

    28.10.2022


    Format / Umfang :

    4392703 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Ethical decision making for autonomous vehicles

    De Moura, Nelson / Chatila, Raja / Evans, Katherine et al. | IEEE | 2020


    ETHICAL DECISION MAKING FOR AUTONOMOUS VEHICLES

    De Moura, Nelson / Chatila, Raja / Evans, Katherine et al. | British Library Conference Proceedings | 2020


    A Three-Level Game-Theoretic Decision-Making Framework for Autonomous Vehicles

    Liu, Mushuang / Wan, Yan / Lewis, Frank L. et al. | IEEE | 2022


    Decision-making techniques for autonomous vehicles

    Villagra, Jorge ;Jiménez, Felipe | TIBKAT | 2023


    Reliable Decision-Making in Autonomous Vehicles

    Alves, Gleifer Vaz / Dennis, Louise / Fernandes, Lucas et al. | Springer Verlag | 2019