Navigational Freespace Detection for Autonomous Driving in Fixed Routes

Aparajit Narayan, Elio Tuci, William Sachiti, Aaron Parsons

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution

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Abstract

Vision-based modules are largely exploited by autonomous driving vehicles to identify the road area and to avoid collisions with other vehicles, pedestrians and obstacles. This paper illustrates the results of a comparative study in which eight different vision-based modules are evaluated for detecting free navigational space in urban environments. All modules are implemented using Convolutions Neural Networks. The distinctive and innovative feature of these modules is the manner via which navigational freespace is identified from image inputs. The modules generate the coordinates of a triangle, whose area represents the navigation freespace. The relative position of the triangle top corner with respect to the image centre points toward the vehicle direction of motion. Thus, when trained on a fixed route, these modules are able to successfully detect the road-freepsace and to make appropriate decisions concerning where to go at roundabouts, intersections etc., in order to reach the final destination.

Original languageEnglish
Title of host publicationESANN 2020 - Proceedings
Subtitle of host publication28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
PublisherESANN (i6doc.com)
Pages715-720
Number of pages6
ISBN (Electronic)9782875870742
ISBN (Print)978-2-87587-073-5
Publication statusPublished - 21 Oct 2020
Event28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020 - Virtual, Online, Belgium
Duration: 2 Oct 20204 Oct 2020

Publication series

NameESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Conference

Conference28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020
Country/TerritoryBelgium
CityVirtual, Online
Period2/10/204/10/20

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