Drivable path detection using CNN sensor fusion for autonomous driving in the snow

Nathir A. Rawashdeh; Jeremy P. Bos; Nader J. Abu-Alrub

doi:10.1117/12.2587993

12 April 2021 Drivable path detection using CNN sensor fusion for autonomous driving in the snow

Nathir A. Rawashdeh, Jeremy P. Bos, Nader J. Abu-Alrub

Proceedings Volume 11748, Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2021; 1174806 (2021) https://doi.org/10.1117/12.2587993
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

This work targets the problem of drivable path detection in poor weather conditions including on snow covered roads. A successful drivable path detection algorithm is vital for safe autonomous driving of passenger cars. Poor weather conditions degrade vehicle perception systems, including cameras, radar, and laser ranging. Convolutional Neural Network (CNN) based multi-modal sensor fusion is applied to path detection. A multi-stream encoder-decoder network that fuses camera, LiDAR, and Radar data is presented here in order to overcome the asymmetrical degradation of sensors by complementing their measurements. The model was trained and tested using a manually labeled subset from the DENSE dataset. Multiple metrics were used to assess the model performance.

Conference Presentation

Citation Download Citation

Nathir A. Rawashdeh, Jeremy P. Bos, and Nader J. Abu-Alrub "Drivable path detection using CNN sensor fusion for autonomous driving in the snow", Proc. SPIE 11748, Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2021, 1174806 (12 April 2021); https://doi.org/10.1117/12.2587993

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