Current driver assistance systems and autonomous driving stacks are limited to well-defined environment conditions and geo fenced areas. To increase driving safety in adverse weather conditions, broadening the application spectrum of autonomous driving and driver assistance systems is necessary. In order to enable this development, reproducible benchmarking methods are required to quantify the expected distortions. In this publication, a testing methodology for disturbances from spray is presented. It introduces a novel lightweight and configurable spray setup alongside an evaluation scheme to assess the disturbances caused by spray. The analysis covers an automotive RGB camera and two different LiDAR systems, as well as downstream detection algorithms based on YOLOv3 and PV-RCNN. In a common scenario of a closely cutting vehicle, it is visible that the distortions are severely affecting the perception stack up to four seconds showing the necessity of benchmarking the influences of spray.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Benchmark for Spray from Nearby Cutting Vehicles


    Contributors:


    Publication date :

    2021-09-19


    Size :

    3042074 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Benchmark spray combustion data for code validation

    Widmann, John / Rao Charagundla, S. / Presser, Cary | AIAA | 2000


    BROADCASTING TELEMATICS DATA TO NEARBY MOBILE DEVICES, VEHICLES, AND INFRASTRUCTURE

    HAYWARD GREGORY L | European Patent Office | 2023

    Free access

    Broadcasting telematics data to nearby mobile devices, vehicles, and infrastructure

    HAYWARD GREGORY L | European Patent Office | 2023

    Free access

    Broadcasting telematics data to nearby mobile devices, vehicles, and infrastructure

    HAYWARD GREGORY | European Patent Office | 2017

    Free access

    Broadcasting telematics data to nearby mobile devices, vehicles, and infrastructure

    HAYWARD GREGORY L | European Patent Office | 2020

    Free access