A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles


The world is moving in the direction of high mobility and dynamics these days as a result of the advancement of technology. In this light, intersection management (IM), which is one of the most important aspects of the transportation industry, calls for a great deal of focus and consideration. Vehicle-to-everything (V2X) Communications enable road entities such as infrastructures, vulnerable road users (VRUs) such as motorcycles, mopeds, scooters, pedestrians, and bicycles, and other types of vehicles such as trucks, buses, cars, emergency vehicles, and railway vehicles like trains or trams to communicate with one another in order to improve traffic safety, efficiency, information and entertainment, and ecological conditions. VRUs include motorcyclists, moped riders, scooter riders, pedestrians, In this paper, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid), and autonomous intersections, and we conduct a comprehensive survey on various intersection management methods for heterogeneous connected vehicles. In addition, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid), and autonomous intersections (CVs). We take into account a wide variety of classes of vehicles, including road vehicles, rail vehicles, and VRUs like bicycles, scooters, and motorcycles, among others. Comprehensive conversations are held regarding a wide variety of intersection goals, modeling, coordination architectures, and scheduling policies. Evaluations are carried out with regard to these parameters at signalized and semi-autonomous intersections. We place a particular emphasis on autonomous intersection management (AIM), and we classify this section according to four primary goals: safety, efficiency, information and entertainment, and the preservation of the natural environment. Each of the intersection goals offers a comprehensive investigation into the relevant body of literature from the aforementioned points of view. In addition, the robustness and resiliency of IM are investigated from a variety of perspectives. These perspectives include sensors, information management and sharing, planning universal scheme, heterogeneous collaboration, vehicle classification, quality measurement, external factors, intersection types, localization faults, Communication anomalies and channel optimization, synchronization, vehicle dynamics and model mismatch, model uncertainties, recovery, security, and privacy.

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