Simulated road following using neuroevolution

Aparajit Narayan*, Elio Tuci, Frédéric Labrosse

*Corresponding author for this work

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


This paper describes a methodology wherein genetic algorithms were used to evolve neural network controllers for application in automatic road driving. The simulated controllers were capable of dynamically varying the mixture of colour components in the input image to ensure the ability to perform well across the entire range of possible environments. During the evolution phase, they were evaluated in a set of environments carefully designed to encourage the development of flexible and general-purpose solutions. Successfully evolved controllers were capable of navigating simulated roads across challenging test environments, each with different geometric and colour distribution properties. These controllers proved to be more robust and adaptable compared to the previous work done using this evolutionary approach. This was due to their improved dynamic colour perception capabilities, as they were now able to demonstrate feature extraction in three (red, green and blue) colour channels.

Original languageEnglish
Title of host publicationArtificial Life and Intelligent Agents - 1st International Symposium, ALIA 2014, Revised Selected Papers
EditorsChristopher J. Headleand, William J. Teahan, Llyr Ap Cenydd
PublisherSpringer Nature
Number of pages14
ISBN (Print)9783319180830
Publication statusPublished - 2015
Event1st International Symposium on Artificial Life and Intelligent Agents, ALIA 2014 - Bangor, United Kingdom of Great Britain and Northern Ireland
Duration: 05 Nov 201406 Nov 2014

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929


Conference1st International Symposium on Artificial Life and Intelligent Agents, ALIA 2014
Country/TerritoryUnited Kingdom of Great Britain and Northern Ireland
Period05 Nov 201406 Nov 2014


  • Active vision
  • Autonomous navigation
  • Dynamic dimensionality reduction
  • Genetic algorithm
  • Neural network
  • Road-following


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