Optimizing Intelligent Vehicular Routing with Edge Computing through Multi-Agent Reinforcement Learning

• Project/Area Number: 24K14913
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 60060:Information network-related
• Research Institution: Toyohashi University of Technology
• Principal Investigator: SHAO XUN 豊橋技術科学大学, 工学(系)研究科(研究院), 准教授 (80774588)
• Project Period (FY): 2024-04-01 – 2027-03-31
• Project Status: Granted (Fiscal Year 2024)
• Budget Amount: ¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000); Fiscal Year 2026: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000); Fiscal Year 2025: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000); Fiscal Year 2024: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
• Keywords: Vehicular network / Edge computing / Cloud computing / Multi-agent algorithm / Reinforcement learning

Outline of Research at the Start

EIRP adopts a cloud-edge-end model, utilizing RSUs for dynamic vehicular packet routing. It enhances route reusability and employs multi-agent reinforcement learning to improve packet delivery rates and minimize communication delays, advancing smart transportation systems.