For autonomous driving in highly dynamic environments, it is anticipated to predict the future behaviors of surrounding vehicles (SVs) and make safe and effective decisions. However, modeling the inherent coupling effect between the prediction and decision-making modules has been a longstanding challenge, especially when there is a need to maintain appropriate computational efficiency. To tackle these problems, we propose a novel integrated intention prediction and decision-making approach, which explicitly models the coupling relationship and achieves efficient computation. Specifically, a spectrum attention net is designed to predict the intentions of SVs by capturing the trends of each frequency component over time and their interrelations. Fast computation of the intention prediction module is attained as the predicted intentions are not decoded to trajectories in the executing process. Furthermore, the proximal policy optimization (PPO) algorithm is employed to address the non-stationary problem in the framework through a modest policy update enabled by a clipping mechanism within its objective function. On the basis of these developments, the intention prediction and decision-making modules are integrated through joint learning. Experiments are conducted in representative traffic scenarios, and the results reveal that the proposed integrated framework demonstrates superior performance over several deep reinforcement learning (DRL) baselines in terms of success rate, efficiency, and safety in driving tasks.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Integrated Intention Prediction and Decision-Making with Spectrum Attention Net and Proximal Policy Optimization


    Contributors:
    Zhou, Xiao (author) / Meng, Chengzhen (author) / Liu, Wenru (author) / Peng, Zengqi (author) / Liu, Ming (author) / Ma, Jun (author)


    Publication date :

    2024-09-24


    Size :

    1495005 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Research on air combat decision algorithm based on proximal policy optimization

    ZHANG Bochao / WEN Xiaoling / LIU Lu et al. | DOAJ | 2023

    Free access

    Thrust Vectored Rocket Landing Integrated Guidance and Control with Proximal Policy Optimization

    De Almeida Souza, Gabriel / Silva, Octavio Mathias / Maximo, Marcos R. O. A. | IEEE | 2022


    Mixed-Autonomy Traffic Control with Proximal Policy Optimization

    Wei, Haoran / Liu, Xuanzhang / Mashayekhy, Lena et al. | IEEE | 2019


    Intention-Aware Dual Attention Based Network for Vehicle Trajectory Prediction

    Yu, Jia / Nie, Linzhen / Zhang, Ming et al. | SAE | 2022


    Intention-Aware Dual Attention Based Network for Vehicle Trajectory Prediction

    Xiao, Yige / Nie, Linzhen / Yin, Zhishuai et al. | British Library Conference Proceedings | 2022