Simulated road following using neuroevolution

Narayan, Aparajit, Tuci, Elio and Labrosse, Frédéric (2015) Simulated road following using neuroevolution. In: ALIA 2014: 1st Artificial Life and Intelligent Agents symposium, 05-06 Nov 2014, Bangor, United Kingdom.

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Abstract

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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Paper published as: Narayan A., Tuci E., Labrosse F. (2015) Simulated Road Following Using Neuroevolution. In: Headleand C., Teahan W., Ap Cenydd L. (eds) Artificial Life and Intelligent Agents. ALIA 2014. Communications in Computer and Information Science, vol 519. Springer, Cham
Research Areas: A. > School of Science and Technology > Computer Science > Artificial Intelligence group
Item ID: 21943
Notes on copyright: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-18084-7_2
Depositing User: Elio Tuci
Date Deposited: 13 Jun 2017 14:21
Last Modified: 04 Apr 2019 15:32
URI: https://eprints.mdx.ac.uk/id/eprint/21943

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