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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    Title :

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


    Contributors:


    Publication date :

    2018-11-01


    Size :

    433509 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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