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Proceedings Paper

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

Paper Details

Date Published: 12 April 2021
PDF: 10 pages
Proc. SPIE 11748, Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2021, 1174806 (12 April 2021); doi: 10.1117/12.2587993
Show Author Affiliations
Nathir A. Rawashdeh, Michigan Technological Univ. (United States)
Jeremy P. Bos, Michigan Technological Univ. (United States)
Nader J. Abu-Alrub, Michigan Technological Univ. (United States)


Published in SPIE Proceedings Vol. 11748:
Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2021
Michael C. Dudzik; Stephen M. Jameson; Theresa J. Axenson, Editor(s)

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