Multi-Agent Reinforcement Learning for Highway Platooning

Abstract

The advent of autonomous vehicles has opened new horizons for transportation efficiency and safety. Platooning, a strategy where vehicles travel closely together in a synchronized manner, holds promise for reducing traffic congestion, lowering fuel consumption, and enhancing overall road safety. This article explores the application of Multi-Agent Reinforcement Learning (MARL) combined with Proximal Policy Optimization (PPO) to optimize autonomous vehicle platooning. We delve into the world of MARL, which empowers vehicles to communicate and collaborate, enabling real-time decision making in complex traffic scenarios. PPO, a cutting-edge reinforcement learning algorithm, ensures stable and efficient training for platooning agents. The synergy between MARL and PPO enables the development of intelligent platooning strategies that adapt dynamically to changing traffic conditions, minimize inter-vehicle gaps, and maximize road capacity. In addition to these insights, this article introduces a cooperative approach to Multi-Agent Reinforcement Learning (MARL), leveraging Proximal Policy Optimization (PPO) to further optimize autonomous vehicle platooning. This cooperative framework enhances the adaptability and efficiency of platooning strategies, marking a significant advancement in the pursuit of intelligent and responsive autonomous vehicle systems.

Architectural Framework to Enhance Image-Based Vehicle Positioning for Advanced Functionalities

“…To further increase traffic flow, ref. [4] extends the idea of single-lane platoons to multi-lane platoons [10][11][12]. Vehicles should consider all available lanes on a given road sector when creating a group of vehicles and travel with small distances between them at high speeds.…”

“…After calculating the orientation of the patch, it can be rotated to a canonical position, enabling the computation of the descriptor and ensuring rotation invariance. BRIEF (9) lacks rotation invariance; hence, ORB employs rotation-aware BRIEF (rBRIEF) (11). ORB integrates this feature while maintaining the speed advantage of BRIEF:…”

CVDMARL: A Communication-Enhanced Value Decomposition Multi-Agent Reinforcement Learning Traffic Signal Control Method

“…With the rapid development of urban motorization, there has been a serious imbalance between traffic demand and supply. Traffic congestion has become a major traffic problem faced by most cities, and its environmental, social, and economic consequences are well documented [1][2][3]. Traffic signal control (TSC) is one of the effective means by which to solve traffic congestion.…”