In various examples, systems and methods are disclosed relating to refinement of safety zones and improving evaluation metrics for the perception modules of autonomous and semi-autonomous systems. Example implementations can exclude areas in the state space that are not safety critical, while retaining the areas that are safety-critical. This can be accomplished by leveraging ego maneuver information and conditioning safety zone computations on ego maneuvers. A maneuver-based decomposition of perception safety zones may leverage a temporal convolution operation with the capability to account for collision at any intermediate time along the way to maneuver completion. This provides a significant reduction in zone volume while maintaining completeness, thus optimizing or otherwise enhancing obstacle perception performance requirements by filtering out regions of state space not relevant to a system's route of travel. Computation of safety-zones conditioned on the ego maneuver greatly reduces excessive conservatism.


    Access

    Download


    Export, share and cite



    Title :

    DETERMINING OBSTACLE PERCEPTION SAFETY ZONES FOR AUTONOMOUS SYSTEMS AND APPLICATIONS


    Contributors:

    Publication date :

    2024-12-05


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    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



    Refining Obstacle Perception Safety Zones via Maneuver-Based Decomposition

    Topan, Sever / Chen, Yuxiao / Schmerling, Edward et al. | IEEE | 2023


    Interaction-Dynamics-Aware Perception Zones for Obstacle Detection Safety Evaluation

    Topan, Sever / Leung, Karen / Chen, Yuxiao et al. | IEEE | 2022


    OBSTACLE PERCEPTION CALIBRATION SYSTEM FOR AUTONOMOUS DRIVING VEHICLES

    JIANG SHU / LUO QI / MIAO JINGHAO et al. | European Patent Office | 2021

    Free access

    Obstacle perception calibration system for autonomous driving vehicles

    JIANG SHU / LUO QI / MIAO JINGHAO et al. | European Patent Office | 2022

    Free access