Advanced driver assistance systems (ADAS) are growing in quantity and complexity, requiring even more sensor effectiveness and computational resources. Even though cameras and LiDAR sensors can provide a steady solution in ideal conditions, they are unreliable for adverse weather conditions, such as rain and dense fog. Therefore, radar technology is used for those circumstances is an option to be used for those circumstances. However, its classification accuracy must be enhanced to compete with the other sensor types. This work presents a solution to enhance radar classification quality, adapting a Convolutional Neural Network (CNN) structure to the input of micro-Doppler images of vulnerable road users (VRUs), such as pedestrians, cyclists, and e-scooter drivers. Moreover, two CNNs were trained, each for a particular region of interest (ROIs), one in the near field of the radar until 15m, and the second one up to 25m. The proposed solution presents a promising overall accuracy for its first trial. Nevertheless, it must be further extended to more complex scenes, including more participants, obstacles, and weather conditions.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Convolutional Neural Network Classification of Vulnerable Road Users based on Micro-Doppler Signatures using an Automotive Radar


    Contributors:


    Publication date :

    2022-10-08


    Size :

    1698232 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    High resolution automotive radar measurements of vulnerable road users – pedestrians & cyclists

    Schubert, Eugen / Meinl, Frank / Kunert, Martin et al. | IEEE | 2015


    RECONFIGURABLE PROPAGATION ENVIRONMENT FOR ENHANCING VULNERABLE ROAD USERS' VISIBILITY TO AUTOMOTIVE RADAR

    Dehkordi, Saeid K. / Caire, Giuseppe | British Library Conference Proceedings | 2021



    Automated Ground Truth Estimation of Vulnerable Road Users in Automotive Radar Data Using GNSS

    Scheiner, Nicolas / Appenrodt, Nils / Dickmann, Jurgen et al. | IEEE | 2019