A unified boundary machine learning model is capable of processing perception data received from various types of perception sensors on an autonomous vehicle to generate perceived boundaries of various semantic boundary types. Such perceived boundaries may then be used, for example, to control the autonomous vehicle, e.g., by generating a trajectory therefor. In some instances, the various semantic boundary types detectable by a unified boundary machine learning model may include at least a virtual construction semantic boundary type associated with a virtual boundary formed by multiple spaced apart construction elements, as well as an additional semantic boundary type associated with one or more other types of boundaries such as boundaries defined by physical barriers, painted or taped lines, road edges, etc.


    Access

    Download


    Export, share and cite



    Title :

    UNIFIED BOUNDARY MACHINE LEARNING MODEL FOR AUTONOMOUS VEHICLES


    Contributors:

    Publication date :

    2025-03-06


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion



    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

    A Unified Evaluation Framework for Autonomous Driving Vehicles

    Roshdi, Myada / Nayeer, Nasif / Elmahgiubi, Mohammed et al. | IEEE | 2020


    A UNIFIED EVALUATION FRAMEWORK FOR AUTONOMOUS DRIVING VEHICLES

    Roshdi, Myada / Nayeer, Nasif / Elmahgiubi, Mohammed et al. | British Library Conference Proceedings | 2020


    Reliable Autonomous Driving Environment Model With Unified State-Extended Boundary

    Jiao, Xinyu / Chen, Junjie / Jiang, Kun et al. | IEEE | 2023