Deep reinforcement learning for cooperative intelligent transportation systems: A survey on architecture, use cases, and future directions

The emergence of Cooperative Intelligent Transportation Systems (C-ITS) has revolutionized urban mobility by enabling seamless collaboration among vehicles, infrastructure, and individuals to improve traffic management, safety, and efficiency. Deep Reinforcement Learning (DRL) has become a key technology in this ecosystem, empowering autonomous agents to make real-time decisions that optimize traffic flow, reduce congestion, and enhance road safety. Although many surveys on Intelligent Transportation Systems (ITS) either overlook cooperative aspects or primarily emphasize security, this paper bridges the gap by examining the diverse applications of DRL in C-ITS. It examines critical areas such as traffic signal control, AV coordination, route planning, and human-vehicle interaction. The study also traces the evolution of DRL algorithms, their adaptation to transportation challenges, and their integration with cutting-edge projects and standards. Additionally, the paper provides a comprehensive analysis of current research trends, identifying achievements, unresolved challenges, and future directions in the field. By synthesizing existing literature and highlighting the synergy between DRL and C-ITS, this survey serves as a valuable resource for researchers, policymakers, and industry professionals striving to develop intelligent, cooperative, and sustainable transportation systems. The insights offered aim to guide advancements in this rapidly growing domain, fostering innovation and practical implementation.