ADAPTIVE Q-LEARNING-BASED IOT INTEGRATION FOR SUSTAINABLE URBAN AUTONOMOUS VEHICLE NAVIGATION

Authors

  • Pannee SUANPANG Suan Dusit University, Thailand
  • Pitchaya JAMJUNTR King Mongkut's University of Technology Thonburi, Thailand
  • Chanchai TECHAWATCHARAPAIKUL King Mongkut's University of Technology Thonburi, Thailand
  • Chutiwan BOONARCHATONG Suan Dusit University, Thailand
  • Wattanapon CHUMPHET Suan Dusit University (Trang Center), Thailand
  • Nawanun SRISUKSAI Suan Dusit University, Thailand

DOI:

https://doi.org/10.14456/aisr.2025.12

Keywords:

Adaptive Q-Learning, Autonomous Vehicles, Navigation, Internet of Things, Sustainability

Abstract

This research explores a novel method for integrating Internet of Things (IoT) with adaptive Q-learning (AQL) to enhance urban autonomous vehicle (AV) navigation for improved sustainability. The core of this method is an AQL algorithm that dynamically modifies learning settings in response to real-time traffic conditions, which optimizes decision-making. The effectiveness of the model was evaluated in a detailed simulation environment designed to reflect the complexity of urban settings. This infrastructure included sensors, communication protocols, and cloud-based systems. The simulation results show substantial advances in route optimization, hazard avoidance, and overall vehicle safety. The results show that integrating AQL with IoT improves the performance of self-driving cars and promotes more ecological and smart urban transportation strategies.

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Published

2025-06-03

How to Cite

SUANPANG, P., JAMJUNTR, P., TECHAWATCHARAPAIKUL, C., BOONARCHATONG, C., CHUMPHET, W., & SRISUKSAI, N. (2025). ADAPTIVE Q-LEARNING-BASED IOT INTEGRATION FOR SUSTAINABLE URBAN AUTONOMOUS VEHICLE NAVIGATION. Asian Interdisciplinary and Sustainability Review, 14(2), Article 1. https://doi.org/10.14456/aisr.2025.12

Issue

Vol. 14 No. 2 (2025): Asian Interdisciplinary and Sustainability Review

Section

Articles

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