Trajectory prediction is one of the key components of the autonomous driving software stack. Accurate prediction for the future movement of surrounding traffic participants is an important prerequisite for ensuring the driving efficiency and safety of intelligent vehicles. Trajectory prediction algorithms based on artificial intelligence have been widely studied and applied in recent years and have achieved remarkable results. However, complex artificial intelligence models are uncertain and difficult to explain, so they may face unintended failures when applied in the real world. In this paper, a self-aware trajectory prediction method is proposed. By introducing a self-awareness module and a two-stage training process, the original trajectory prediction module's performance is estimated online, to facilitate the system to deal with the possible scenario of insufficient prediction function in time, and create conditions for the realization of safe and reliable autonomous driving. Comprehensive experiments and analysis are performed, and the proposed method performed well in terms of self-awareness, memory footprint, and real-time performance, showing that it may serve as a promising paradigm for safe autonomous driving.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Self-Aware Trajectory Prediction for Safe Autonomous Driving


    Contributors:
    Shao, Wenbo (author) / Li, Jun (author) / Wang, Hong (author)


    Publication date :

    2023-06-04


    Size :

    2345596 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Implicit Scene Context-Aware Interactive Trajectory Prediction for Autonomous Driving

    Lan, Wenxing / Li, Dachuan / Hao, Qi et al. | IEEE | 2024


    BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving

    Liao, Haicheng / Li, Zhenning / Shen, Huanming et al. | ArXiv | 2023

    Free access

    AI-TP: Attention-Based Interaction-Aware Trajectory Prediction for Autonomous Driving

    Zhang, Kunpeng / Zhao, Liang / Dong, Chengxiang et al. | IEEE | 2023


    GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving

    Li, Xin / Ying, Xiaowen / Chuah, Mooi Choo | ArXiv | 2019

    Free access

    Delay-aware Robust Control for Safe Autonomous Driving

    Kalaria, Dvij / Lin, Qin / Dolan, John M. | IEEE | 2022