Road Detection using Convolutional Neural Networks

Aparajit Narayan, Elio Tuci, Frédéric Labrosse, Muhanad H. Mohammed Alkilabi

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)


The work presented in this paper aims to address the problem of autonomous driving (especially along ill-defined roads) by using convolutional neural networks to predict the position and width of roads from camera input images. The networks are trained with supervised learning (i.e., back-propagation) using a dataset of annotated road images. We train two different network architectures for images corresponding to six colour models. They are tested “off-line” on a road detection task using image sequences not used in training. To benchmark our approach, we compare the performance of our networks with that of a different image processing method that relies on differences in colour distribution between the road and non-road areas of the camera input. Finally, we use a trained convolutional network to successfully navigate a Pioneer 3-AT robot on 5 distinct test paths. Results show that the network can safely guide the robot in this navigation task and that it is robust enough to deal with circumstances much different from those encountered during training.

Original languageEnglish
Title of host publicationProceedings of the 14th European Conference on Artificial Life, ECAL 2017
PublisherMIT Press Journals
Number of pages8
ISBN (Electronic)9780262346337
Publication statusPublished - 2017
Event14th European Conference on Artificial Life, ECAL 2017 - Lyon, France
Duration: 04 Sept 201708 Sept 2017

Publication series

NameProceedings of the 14th European Conference on Artificial Life, ECAL 2017


Conference14th European Conference on Artificial Life, ECAL 2017
Period04 Sept 201708 Sept 2017


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