Vol. 3 No. 2 (2023): African Journal of Artificial Intelligence and Sustainable Development
Articles

Machine Learning for Autonomous Vehicle Decision Support Systems

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  • Dr. Daniel Nikulin
Dr. Daniel Nikulin
Professor of Electrical Engineering, National Research University – Moscow Institute of Electronic Technology (MIET), Russia
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Published 08-11-2023

Keywords

  • AI technologies
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Daniel Nikulin, “Machine Learning for Autonomous Vehicle Decision Support Systems”, African J. of Artificial Int. and Sust. Dev., vol. 3, no. 2, pp. 121–144, Nov. 2023, Accessed: Dec. 25, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/117

Abstract

Most perception, cognition, and execution functions in the AD software stack are deeply relying on the AI algorithms. Generally, AD algorithms can be divided into two main groups; data-driven or non-data-driven algorithms. The first group consists of finite state machines (FSMs), rule-based systems, dynamic programming and game theory [1]. The FSMs are the simplest algorithms among these ones, and consisting state representation of the world and constructed developmental tree are the most valuable powerful points in this approach. In the last data-driven group, AD algorithms can be categorized into three main categories. The first one has fully dependent on data and deep learning or machine learning applies these kind of algorithms and effective on making most valuable driving-related decision [2].

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