Vol. 4 No. 1 (2024): African Journal of Artificial Intelligence and Sustainable Development
Articles

Machine Learning for Adaptive Cruise Control in Autonomous Vehicles

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  • Dr. Mohammad Simaan
Dr. Mohammad Simaan
Professor of Electrical Engineering, American University of Beirut (AUB)
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Published 23-04-2024

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How to Cite

[1]
Dr. Mohammad Simaan, “Machine Learning for Adaptive Cruise Control in Autonomous Vehicles”, African J. of Artificial Int. and Sust. Dev., vol. 4, no. 1, pp. 140–167, Apr. 2024, Accessed: Nov. 06, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/97

Abstract

We aim to illustrate the development process of a particular control system embedded in a real car. More precisely, a common adaptive speed control of an automobile will be examined, which stands on an ACC system generalized by a connected cruise control (CCC). The proposed method in the current work corresponds to the first stage where the centralized control of the car is conducted based on the ML techniques such as the Reinforcement learning. This work represents the immediate control of the car in each time step based on the known accurate models of prior state and the prior control action, thus a Model-based Reinforcement Learning (MBRL) method was used to address the mentioned problem. The full self-driving car in which the responsibility of driving is shifted from the human driver to a controller system. Given the fact that in some cases these autonomous vehicles operate in the presence of human counterparts, the problem is redefined as behavior planning and control of an autonomous vehicle considering the possible intention changes and actions the human driver might perform.

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