From the early stage of robotic applications to current autonomous driving technologies, environment modeling has been acting as the middleware for connecting perception and decision layers. In robotic applications, space-oriented models (e.g., grid map, drivable area) are widely applied to faithfully reflect the space occupation. With the development of autonomous driving, highly dynamic and complex road environment brings rising need to understand the type and motion status of objects, thus element list has became the mainstream environment model. However, along comes the reliablity problem caused by missed detection and irregular objects, which is still inevitable despite the detection accuracy improvement. In view of this, a new view of driving environment is proposed as the unified state-extended boundary (USEB), aiming to improve the reliablity of element-oriented model. For driving decision requirements, different types of elements are consistently converted into driving constraints. Semantics and dynamics are expressed as the status of drivable area boundary, making it possible to merge space occupation to improve reliability against missed detection and irregular objects. Evaluation of USEB is carried out on the nuScenes dataset. Comparative results show that the proposed USEB could cover the required information for driving decision, whereas achieving higher reliability than the commonly applied element-oriented model.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Reliable Autonomous Driving Environment Model With Unified State-Extended Boundary


    Contributors:
    Jiao, Xinyu (author) / Chen, Junjie (author) / Jiang, Kun (author) / Wang, Yunlong (author) / Cao, Zhong (author) / Yang, Mengmeng (author) / Yang, Diange (author)


    Publication date :

    2023-01-01


    Size :

    3760560 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Autonomous Driving Risk Assessment With Boundary-Based Environment Model

    Jiao, Xinyu / Chen, Junjie / Jiang, Kun et al. | IEEE | 2024



    UNIFIED BOUNDARY MACHINE LEARNING MODEL FOR AUTONOMOUS VEHICLES

    CHAABANE MOHAMED / KAPLAN BENJAMIN / LITVIN YEVGENI et al. | European Patent Office | 2025

    Free access

    UNIFIED BOUNDARY MACHINE LEARNING MODEL FOR AUTONOMOUS VEHICLES

    CHAABANE MOHAMED / KAPLAN BENJAMIN / LITVIN YEVGENI et al. | European Patent Office | 2025

    Free access

    UNIFIED BOUNDARY MACHINE LEARNING MODEL FOR AUTONOMOUS VEHICLES

    CHAABANE MOHAMED / KAPLAN BENJAMIN / LITVIN YEVGENI et al. | European Patent Office | 2025

    Free access