Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/109291
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Type: Journal article
Title: Ahura: a heuristic-based racer for the open racing car simulator
Author: Bonyadi, M.
Michalewicz, Z.
Nallaperuma, S.
Neumann, F.
Citation: IEEE Transactions on Computational Intelligence and AI in Games, 2017; 9(3):290-304
Publisher: IEEE
Issue Date: 2017
ISSN: 1943-068X
1943-0698
Statement of
Responsibility: 
Mohammad Reza Bonyadi, Zbigniew Michalewicz, Samadhi Nallaperuma, Frank Neumann
Abstract: Designing automatic drivers for car racing is an active field of research in the area of robotics and artificial intelligence. A controller called Ahura (a heuristic-based racer) for the open racing car simulator is proposed in this paper. Ahura includes five modules, namely steer controller, speed controller, opponent manager, dynamic adjuster, and stuck handler. These modules have 23 parameters all together that are tuned using an evolutionary strategy for a particular car to ensure fast and safe drive on different tracks. These tuned parameters are further modified by the dynamic adjuster module during the run according to the width, friction, and dangerous zones of the track. The dynamic adjustment enables Ahura to decide on-the-fly based on the current situation; hence, it eliminates the need for prior knowledge about the characteristics of the track. The driving performance of Ahura is compared with other state-of-the-art controllers on 40 tracks when they drive identical cars. Our experiments indicate that Ahura performs significantly better than other controllers in terms of damage and completion time especially on complex tracks (road tracks). Also, experiments show that the overtaking strategy of Ahura is safer and more effective compared to other controllers.
Keywords: Sensors; wheels; acceleration; gears; automobiles; Australia automatic car racing; car racing simulator; evolutionary strategy; neural networks
Rights: © 2017, IEEE
DOI: 10.1109/TCIAIG.2016.2565661
Grant ID: http://purl.org/au-research/grants/arc/DP130104395
Appears in Collections:Aurora harvest 3
Computer Science publications

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