ISSN: 2582-8266 (Online) || ISSN Approved Journal || Google Scholar Indexed || Impact Factor: 9.48 || Crossref DOI
Reinforcement learning for autonomous vehicle navigation in Urban environments
Student, Computer Science, University of Virginia, USA.
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2024, 11(01), 457-466.
Article DOI: 10.30574/wjaets.2024.11.1.0019
Publication history:
Received on 06 December 2023; revised on 21 January 2024; accepted on 24 January 2024
Abstract:
This paper presents a comprehensive study on the application of reinforcement learning (RL) techniques for autonomous vehicle navigation in complex urban environments. We propose a novel deep RL framework that combines state-of-the-art algorithms with realistic urban traffic simulations to train robust navigation policies. Our approach leverages a hierarchical learning structure to decompose the challenging urban driving task into more manageable sub-tasks. Extensive experiments in simulated urban scenarios demonstrate that our method significantly outperforms baseline approaches in terms of safety, efficiency, and adaptability to diverse traffic conditions. We also conduct real-world validation tests to verify the transferability of learned policies to actual autonomous vehicles. Our results highlight the potential of RL-based techniques to enable safe and efficient autonomous navigation in dynamic city environments.
Keywords:
Reinforcement Learning; Autonomous Vehicles; Urban Navigation; Hierarchical Learning; Traffic Simulation; Deep Learning; Self-Driving Cars; Policy Optimization; Urban Environments; Safety Systems
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0