機械知覚&ロボティクスグループ
中部大学

Deep Learning 国際会議

Pedestrian-Detection Method Based on 1D-CNN During LiDAR Rotation

Author
Yuki Kunisada, Takayoshi Yamashita and Hironobu Fujiyoshi
Publication
The 21st IEEE International Conference on Intelligent Transportation Systems (ITSC), 2018

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 (1D-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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