Cooperative MARL-PPO Approach for Automated Highway Platoon Merging

This paper presents a cooperative highway platooning strategy that integrates Multi-Agent Reinforcement Learning (MARL) with Proximal Policy Optimization (PPO) to effectively manage the complex task of merging. In modern transportation systems, platooning—where multiple vehicles travel closely toget...

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Bibliographic Details
Published in:Electronics (Basel) 2024-08, Vol.13 (15), p.3102
Main Authors: Kolat, Máté, Bécsi, Tamás
Format: Article
Language:English
Subjects:
Algorithms
Automation
Communication
Decision making
Efficiency
Energy consumption
Energy efficiency
Methods
Multiagent systems
Optimization
Platooning
Railroad accidents & safety
Roads & highways
Robotics
Traffic control
Transportation systems
Trucks
Vehicles
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Summary:This paper presents a cooperative highway platooning strategy that integrates Multi-Agent Reinforcement Learning (MARL) with Proximal Policy Optimization (PPO) to effectively manage the complex task of merging. In modern transportation systems, platooning—where multiple vehicles travel closely together under coordinated control—promises significant improvements in traffic flow and fuel efficiency. However, the challenge of merging, which involves dynamically adjusting the formation to incorporate new vehicles, remains challenging. Our approach leverages the strengths of MARL to enable individual vehicles within a platoon to learn optimal behaviors through interactions. PPO ensures stable and efficient learning by optimizing policies balancing exploration and exploitation. Simulation results show that our method achieves merging with safety and operational efficiency.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13153102