Seamlessly operating an autonomous vehicles in a crowded pedestrian environment is a very challenging task. This is because human movement and interactions are very hard to predict in such environments. Recent work has demonstrated that reinforcement learning-based methods have the ability to learn to drive in crowds. However, these methods can have very poor performance due to inaccurate predictions of the pedestrians’ future state as human motion prediction has a large variance. To overcome this problem, we propose a new method, SARL-SGAN-KCE, that combines a deep socially aware attentive value network with a human multimodal trajectory prediction model to help identify the optimal driving policy. We also introduce a novel technique to extend the discrete action space with minimal additional computational requirements. The kinematic constraints of the vehicle are also considered to ensure smooth and safe trajectories. We evaluate our method against the state of art methods for crowd navigation and provide an ablation study to show that our method is safer and closer to human behaviour.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Socially Aware Crowd Navigation with Multimodal Pedestrian Trajectory Prediction for Autonomous Vehicles


    Contributors:
    Li, Kunming (author) / Shan, Mao (author) / Narula, Karan (author) / Worrall, Stewart (author) / Nebot, Eduardo (author)


    Publication date :

    2020-09-20


    Size :

    794301 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Socially Aware Crowd Navigation with Multimodal Pedestrian Trajectory Prediction for Autonomous Vehicles

    Li, Kunming / Shan, Mao / Narula, Karan et al. | ArXiv | 2020

    Free access

    Pedestrian Dominance Modeling for Socially-Aware Robot Navigation

    Randhavane, Tanmay / Bera, Aniket / Kubin, Emily et al. | ArXiv | 2018

    Free access

    Enhancing Pedestrian Trajectory Prediction with Crowd Trip Information

    Tamaru, Rei / Li, Pei / Ran, Bin | ArXiv | 2024

    Free access

    A context-aware pedestrian trajectory prediction framework for automated vehicles

    Kalatian, Arash / Farooq, Bilal | ArXiv | 2021

    Free access

    Crowd Prediction and Autonomous Navigation with Partial Observations

    Li, Kunming / Shan, Mao / Worrall, Stewart et al. | IEEE | 2022