Drivable path detection using CNN sensor fusion for autonomous driving in the snow
Department of Electrical and Computer Engineering
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.
Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2021
Abu-Alrub, N. J.
Drivable path detection using CNN sensor fusion for autonomous driving in the snow.
Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2021,
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/14966