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ADAPTIVE Q-LEARNING-BASED IOT INTEGRATION FOR SUSTAINABLE URBAN AUTONOMOUS VEHICLE NAVIGATION |
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| รหัสดีโอไอ | |
| Creator | Pannee SUANPANG |
| Title | ADAPTIVE Q-LEARNING-BASED IOT INTEGRATION FOR SUSTAINABLE URBAN AUTONOMOUS VEHICLE NAVIGATION |
| Contributor | Pitchaya JAMJUNTR, Chanchai TECHAWATCHARAPAIKUL, Chutiwan BOONARCHATONG, Wattanapon CHUMPHET, Nawanun SRISUKSAI |
| Publisher | Asian Interdisciplinary and Sustainability Review |
| Publication Year | 2568 |
| Journal Title | Asian Interdisciplinary and Sustainability Review |
| Journal Vol. | 14 |
| Journal No. | 2 |
| Page no. | Article 1 |
| Keyword | Adaptive Q-Learning, Autonomous Vehicles, Navigation, Internet of Things, Sustainability |
| URL Website | https://so05.tci-thaijo.org/index.php/PSAKUIJIR |
| Website title | https://so05.tci-thaijo.org/index.php/PSAKUIJIR/article/view/279880 |
| ISSN | 3027-6535 |
| 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. |
