Pedestrian detection in autonomous driving systems is important for preventing accidents involving pedestrians and vehicles. Conventional pedestrian detection methods involve Light Detection and Ranging (LiDAR), which requires clustering points into a cloud before determining whether each point is a pedestrian. Therefore, there may not be sufficient time for an autonomous driving system to ensure safety if a pedestrian and vehicle are too close to each other. We propose a pedestrian detection method that is based on a one-dimensional convolution neural network (lD-CNN) that processes LiDAR waveform data without delay. The proposed method sequentially inputs LiDAR waveform data to the 1D-CNN and determines whether each point belongs to a pedestrian. Therefore, it is possible to reduce the difference between the detected and actual positions of pedestrian since our method can be used during LiDAR sensor rotation.


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    Titel :

    Pedestrian-Detection Method based on 1D-CNN during LiDAR Rotation


    Beteiligte:


    Erscheinungsdatum :

    01.11.2018


    Format / Umfang :

    433509 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    LIDAR and vision-based pedestrian detection system

    Premebida, C. / Ludwig, O. / Nunes, U. | British Library Online Contents | 2009


    Context-aware pedestrian detection using LIDAR

    Oliveira, L / Nunes, U | IEEE | 2010


    Exploiting LIDAR-based features on pedestrian detection in urban scenarios

    Premebida, Cristiano / Ludwig, Oswaldo / Nunes, Urbano | IEEE | 2009



    Context-Aware Pedestrian Detection Using LIDAR, pp. 773-778

    Oliveira, L. / Institute of Electrical and Electronics Engineers | British Library Conference Proceedings | 2010