In the development of autonomous vehicles (AVs), ensuring safety and reliability is of paramount importance, especially when considering the presence of pedestrians and their behavior. This necessitates the implementation of robust motion control algorithms. Addressing the vehicle decision-making challenge in pedestrian-populated environments, we have devised an interactive pedestrian model that effectively combines a social force-based approach with pedestrian crossing intentions. By integrating these elements, our model can accurately determine pedestrian trajectories influenced by AVs. To further enhance the decision-making process, we introduce a framework rooted in social preference theory and deep reinforcement learning (DRL). This framework facilitates the generation of diverse decision strategies, accommodating various driving styles. To validate our proposed framework, we conduct rigorous network training and simulation experiments, systematically comparing the strategies derived from DRL algorithms with multiple control experiments. Our comprehensive evaluation demonstrates that the learning-based framework not only fosters smoother and more natural interactions between AVs and pedestrians but also enhances overall safety during such encounters.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Research on Interaction Strategies of Autonomous Vehicles Based on Social Preference


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Qu, Yi (editor) / Gu, Mancang (editor) / Niu, Yifeng (editor) / Fu, Wenxing (editor) / Hu, Chungang (author) / Zhou, Zhuping (author) / Sun, Leyi (author) / Nian, Xinyi (author) / Chen, Zheng (author)

    Conference:

    International Conference on Autonomous Unmanned Systems ; 2023 ; Nanjing, China September 09, 2023 - September 11, 2023



    Publication date :

    2024-04-27


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





    INDIVIDUAL DRIVING PREFERENCE ADAPTED COMPUTERIZED ASSIST OR AUTONOMOUS DRIVING OF VEHICLES

    HEALEY JENNIFER A / ZAFIROGLU ALEXANDRA C | European Patent Office | 2021

    Free access

    Method and System to Recognize Individual Driving Preference for Autonomous Vehicles

    YANG I-HSUAN / LI LIYUN / MIAO JINGHAO et al. | European Patent Office | 2018

    Free access


    AUTHENTICATED INTERACTION FOR AUTONOMOUS VEHICLES

    KIRK STEPHEN JAMES DOUGLASS | European Patent Office | 2025

    Free access