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Electronics 2015, 4(4), 739-762; doi:10.3390/electronics4040739

Reactive Planning of Autonomous Vehicles for Traffic Scenarios

1,†,* and 2,†
1
Robotics and Artificial Intelligence Laboratory, Indian Institute of Information Technology, Allahabad 211012, India
2
Vice Chancellors Office, Coventry University, Priory Street, Coventry CV1 5FB, UK
The author contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Vicente Milanés
Received: 16 July 2015 / Revised: 30 September 2015 / Accepted: 6 October 2015 / Published: 9 October 2015
(This article belongs to the Special Issue Intelligent and Cooperative Vehicles)
View Full-Text   |   Download PDF [1160 KB, uploaded 9 October 2015]   |  

Abstract

Autonomous vehicles operate in real time traffic scenarios and aim to reach their destination from their source in the most efficient manner possible. Research in mobile robotics provides a variety of sophisticated means with which to plan the path of these vehicles. Conversely professional human drivers usually drive using instinctive means, which enables them to reach their goal almost optimally whilst still obeying all traffic laws. In this paper we propose the use of fuzzy logic for novel motion planning. The planner is generated using an evolutionary algorithm which resembles the learning stage of professional drivers. Whether to overtake or not, is a decision which affects one’s driving and the decision is made using some deliberation. We further extend the approach to perform decision making regarding overtaking for all vehicles. Further we coordinate the motion of the vehicles at a traffic crossing to avoid any potential jam or collision. Experimental results prove that by using this approach we have been able to make the vehicles move in an optimal manner in a variety of scenarios. View Full-Text
Keywords: autonomous vehicles; motion planning; fuzzy logic; evolutionary fuzzy inference systems; traffic simulation; multi-robot motion planning autonomous vehicles; motion planning; fuzzy logic; evolutionary fuzzy inference systems; traffic simulation; multi-robot motion planning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Kala, R.; Warwick, K. Reactive Planning of Autonomous Vehicles for Traffic Scenarios. Electronics 2015, 4, 739-762.

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