Road Boundary Detection Using Ant Colony Optimization Algorithm
2020 (English)In: Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery: Volume 1 / [ed] Yong Liu; Lipo Wang; Liang Zhao; Zhengtao Yu, Springer , 2020, p. 409-416Conference paper, Published paper (Refereed)
Abstract [en]
A common problem for autonomous vehicles is to define a coherent round boundary of unstructured roads. To solve this problem an evolutionary approach has been evaluated, by using a modified ant optimization algorithm to define a coherent road edge along the unstructured road in night conditions. The work presented in this paper involved pre-processing, perfecting the edges in an autonomous fashion and developing an algorithm to find the best candidates of starting positions for the ant colonies. All together these efforts enable ant colony optimization (ACO) to perform successfully in this application scenario. The experiment results show that the best paths well followed the edges and that the mid-points between the paths stayed centered on the road.
Place, publisher, year, edition, pages
Springer , 2020. p. 409-416
Series
Advances in Intelligent Systems and Computing, ISSN 2194-5357, E-ISSN 2194-5365 ; 1074
Keywords [en]
Ant colony optimization, Autonomous vehicles, Lane detection
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-46631DOI: 10.1007/978-3-030-32456-8_44Scopus ID: 2-s2.0-85077006690ISBN: 9783030324551 (print)OAI: oai:DiVA.org:mdh-46631DiVA, id: diva2:1382086
Conference
15th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th International Conference on Harmony Search, Soft Computing and Applications, ICHSA 2019; Kunming; China; 20 July 2019 through 22 July 2019
2020-01-022020-01-022022-12-08Bibliographically approved