As self-driving vehicles continue to gain traction worldwide, the demand for robust safety systems, particularly concerning pedestrian's safety, has become increasingly critical. This paper introduces a pedestrian collision avoidance strategy that focuses on detecting pedestrians and estimating their distance from the vehicle. The key contributions of this approach include: (1) the detection of multiple pedestrians using an onboard vehicle camera, achieved through the training of a neural network; (2) the estimation of pedestrian distance by integrating Lidar point cloud data onto the camera's 2D imagery; and (3) the implementation of a responsive control system that overrides the vehicle's default controller to stop the vehicle when pedestrians are detected within a dangerous proximity.


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    Titel :

    Pedestrian-Collision Avoidance Strategy Using Deep Neural Network


    Beteiligte:


    Erscheinungsdatum :

    17.12.2024


    Format / Umfang :

    1184945 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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