In autonomous vehicle (AV) technology, the ability to accurately predict the movements of surrounding vehicles is paramount for ensuring safety and operational efficiency. Incorporating human decision-making insights enables AVs to more effectively anticipate the potential actions of other vehicles, significantly improving prediction accuracy and responsiveness in dynamic environments. This paper introduces the Human-Like Trajectory Prediction (HLTP) model, which adopts a teacher-student knowledge distillation framework inspired by human cognitive processes. The HLTP model incorporates a sophisticated teacher-student knowledge distillation framework. The “teacher” model, equipped with an adaptive visual sector, mimics the visual processing of the human brain, particularly the functions of the occipital and temporal lobes. The “student” model focuses on real-time interaction and decision-making, drawing parallels to prefrontal and parietal cortex functions. This approach allows for dynamic adaptation to changing driving scenarios, capturing essential perceptual cues for accurate prediction. Evaluated using the Macao Connected and Autonomous Driving (MoCAD) dataset, along with the NGSIM and HighD benchmarks, HLTP demonstrates superior performance compared to existing models, particularly in challenging environments with incomplete data.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Cognitive-Based Trajectory Prediction Approach for Autonomous Driving


    Contributors:

    Published in:

    Publication date :

    2024-04-01


    Size :

    4054404 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Trajectory-Based Failure Prediction for Autonomous Driving

    Kuhn, Christopher B. / Hofbauer, Markus / Petrovic, Goran et al. | IEEE | 2021


    Intention-Driven Trajectory Prediction for Autonomous Driving

    Fan, Shiwei / Li, Xiangxu / Li, Fei | IEEE | 2021


    Graph-based Planning-informed Trajectory Prediction for Autonomous Driving

    Dong, Qing / Jiang, Titong / Xu, Tao et al. | IEEE | 2022


    Self-Aware Trajectory Prediction for Safe Autonomous Driving

    Shao, Wenbo / Li, Jun / Wang, Hong | IEEE | 2023


    Motion Query-based Multimodal Vehicle Trajectory Prediction for Autonomous Driving

    Jiang, Hao / Niu, Yixun / Hu, Chuan et al. | IEEE | 2023