Virtual LiDAR: self-driving scenes classification

Binh Tran; Hongbo Zhang; Shawn Addington

doi:10.1117/12.2576003

20 September 2020 Virtual LiDAR: self-driving scenes classification

Binh Tran, Hongbo Zhang, Shawn Addington

Proceedings Volume 11533, Image and Signal Processing for Remote Sensing XXVI; 1153310 (2020) https://doi.org/10.1117/12.2576003
Event: SPIE Remote Sensing, 2020, Online Only

Abstract

In this research, we propose the use of end-to-end deep learning simulation approach for assisting the design of LiDAR. The results show that two million points per second rate is optimal for point cloud based intersection classification task. The detection range of up to 100 meters corresponds to optimal classification performance. The 10 degree of upper field of view and 10 degree of lower field of view is sufficient for intersection classification. A linear increase of classification accuracy from 10 to 70 channels is evident. The research bridges the gap of lower level LiDAR simulation and development and self-driving visual tasks and expected to find applications to improve self-driving performance and safety.

Conference Presentation

Citation Download Citation

Binh Tran, Hongbo Zhang, and Shawn Addington "Virtual LiDAR: self-driving scenes classification", Proc. SPIE 11533, Image and Signal Processing for Remote Sensing XXVI, 1153310 (20 September 2020); https://doi.org/10.1117/12.2576003

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