Current autonomous driving technologies are being rolled out in geo-fenced areas with well-defined operation conditions such as time of operation, area, weather conditions and road conditions. In this way, challenging conditions as adverse weather, slippery road or densely-populated city centers can be excluded. In order to lift the geo-fenced restriction and allow a more dynamic availability of autonomous driving functions, it is necessary for the vehicle to autonomously perform an environment condition assessment in real time to identify when the system cannot operate safely and either stop operation or require the resting passenger to take control. In particular, adverse-weather challenges are a fundamental limitation as sensor performance degenerates quickly, prohibiting the use of sensors such as cameras to locate and monitor road signs, pedestrians or other vehicles. To address this issue, we train a deep learning model to identify outdoor weather and dangerous road conditions, enabling a quick reaction to new situations and environments. We achieve this by introducing an improved taxonomy and label hierarchy for a state-of-the-art adverse-weather dataset, relabelling it with a novel semi-automated labeling pipeline. Using the novel proposed dataset and hierarchy, we train RECNet, a deep learning model for the classification of environment conditions from a single RGB frame. We outperform baseline models by relative 16% in F1-Score, while maintaining a real-time capable performance of 20 Hz. The code is published here1.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Real-time Environment Condition Classification for Autonomous Vehicles


    Contributors:


    Publication date :

    2024-06-02


    Size :

    1397911 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Real-time coordination of autonomous vehicles

    Bouroche, M. / Hughes, B. / Cahill, V. | IEEE | 2006


    Real time cooperative localization for autonomous vehicles

    Bounini, Farid / Gingras, Denis / Pollart, Herve et al. | IEEE | 2016


    Real-time simulator of collaborative autonomous vehicles

    Bounini, Farid / Gingras, Denis / Lapointe, Vincent et al. | IEEE | 2014


    Adaptive real-time streaming for autonomous vehicles

    CAMPBELL CAITLIN | European Patent Office | 2023

    Free access