Automated Intersection Management for Connected and Automated Vehicles: A Twenty-Five-Year Review

Overview & Synopsis

Automated Intersection Management (AIM) has emerged as a promising paradigm for improving the safety and efficiency of urban intersections in the context of Connected and Automated Vehicles (CAVs). By relying on vehicleto-vehicle, vehicle-to-infrastructure, and more generally vehicleto-everything communications, AIM approaches enable vehicles and infrastructure to exchange fine-grained information and coordinate intersection crossing at the vehicle level. This paper reviews twenty-five years of research on communication-based automated intersection management. It analyzes the historical evolution of the field, from early reservation-based frameworks to optimization, formal safety control, decentralized coordination, mixed-traffic scenarios, and learning-based approaches. A taxonomy of major coordination strategies is proposed, and the role of V2V, V2I, and V2X communications is discussed with respect to latency, reliability, scalability, and security. The paper also reviews evaluation methodologies, performance metrics, and current limitations of simulation-based studies. Finally, it identifies key open challenges for large-scale deployment, including mixed traffic, communication uncertainty, vulnerable road users, standardized benchmarks, cybersecurity, edge intelligence, and digital-twin-based validation. The review argues that future progress will depend on the joint integration of communicationaware coordination, distributed intelligence, uncertainty-aware control, and robust cyber-physical transportation architectures.