Vol. 2 No. 2 (2022): Journal of AI-Assisted Scientific Discovery
Articles

Computational Intelligence for Adaptive Traffic Signal Control in Autonomous Vehicle Environments

Dr. Haejin Kim
Professor of Computer Science, Ulsan National Institute of Science and Technology (UNIST), South Korea
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Published 30-12-2022

How to Cite

[1]
Dr. Haejin Kim, “Computational Intelligence for Adaptive Traffic Signal Control in Autonomous Vehicle Environments”, Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, pp. 75–96, Dec. 2022, Accessed: Nov. 22, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/73

Abstract

This paper introduces CI-adaptive models, which extend the capabilities of traffic controllers, by modeling interactions among a few major intersections and provide useful future knowledge by means of a pre-estimation study. The estimation model is employed to allow drivers and control centers to make plans regarding intersection crossing, encouraged by the support from a communication system, which is automatically acting in cooperation with an adaptive signal control system.

In this context, Computational Intelligent (CI) adaptive models appear as a potential solution to offer sustainable urban traffic management. The main idea is transitioning the focus from vehicles to intersections and from both individual and instantaneous to collective and time-agnostic control. This collective approach concerns the decisions of some major intersections, attributing to explicit collective behavior performed by drivers in the vicinity of these intersections.

Future developments of an autonomous vehicle will challenge current traffic control systems in a manner that is yet to be fully understood. For instance, autonomous vehicles, equipped with a coordination and communication system, could potentially reduce intersection delays by selecting speeds to enable uninterrupted crossing of intersections, or even altering speeds to catch traffic light greens.

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