Vol. 3 No. 1 (2023): African Journal of Artificial Intelligence and Sustainable Development
Articles

Autonomous Vehicle Path Planning Using Deep Reinforcement Learning

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  • Dr. Sarah Hassan
Dr. Sarah Hassan
Professor of Computer Science, Zewail City of Science and Technology, Egypt
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Published 14-05-2023

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

How to Cite

[1]
Dr. Sarah Hassan, “Autonomous Vehicle Path Planning Using Deep Reinforcement Learning”, African J. of Artificial Int. and Sust. Dev., vol. 3, no. 1, pp. 277–307, May 2023, Accessed: Nov. 07, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/86

Abstract

Deep reinforcement learning (DRL) has successfully been used to solve various nonlinear and complex problem domains such as those characterized by continuous states or actions. Atari games, robot control, and path planning are working examples of such cases. The quality of the learned controller, however, is heavily dependent on both the accuracy and size of the data used for learning. In addition, the generation of DRL trajectories is extremely slow and their quality and smoothness can vary greatly depending on the discrete nature of the chosen algorithm [1] [2]. Given these challenges, we aim to introduce an advanced adaptation of the existing DRL algorithm for solving the path-planning issue in an autonomous vehicle-targeting environment safety-critical problem domain. This research also targets reduced transit time and smoother and less aggressive behavior.

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