Fast LIDAR-based road detection using fully convolutional neural networks
Paper in proceeding, 2017

In this work, a deep learning approach has been developed to carry out road detection using only LIDAR data. Starting from an unstructured point cloud, top-view images encoding several basic statistics such as mean elevation and density are generated. By considering a top-view representation, road detection is reduced to a single-scale problem that can be addressed with a simple and fast fully convolutional neural network (FCN). The FCN is specifically designed for the task of pixel-wise semantic segmentation by combining a large receptive field with high-resolution feature maps. The proposed system achieved excellent performance and it is among the top-performing algorithms on the KITTI road benchmark. Its fast inference makes it particularly suitable for real-Time applications.


Image coding

Intelligent vehicle highway systems



Neural networks


Optical radar


Luca Caltagirone

Chalmers, Applied Mechanics, Vehicle Engineering and Autonomous Systems

Samuel Scheidegger

Chalmers, Signals and Systems

Lennart Svensson

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

Mattias Wahde

Chalmers, Applied Mechanics, Vehicle Engineering and Autonomous Systems

IEEE Intelligent Vehicles Symposium, Proceedings


COPPLAR CampusShuttle cooperative perception & planning platform

VINNOVA (2015-04849), 2016-01-01 -- 2018-12-31.

Areas of Advance


Subject Categories

Computer Science

Computer Systems

Computer Vision and Robotics (Autonomous Systems)



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