Vehicle-to-vehicle channel modeling and measurements: recent advances and future challenges
IEEE Communications Magazine
Connectivity statistics of store-and-forward intervehicle communication
IEEE Transactions on Intelligent Transportation Systems
Cluster-based multi-channel communications protocols in vehicle ad hoc networks
IEEE Wireless Communications
A vision-based approach to collision prediction at traffic intersections
IEEE Transactions on Intelligent Transportation Systems
Reliable Detection of Overtaking Vehicles Using Robust Information Fusion
IEEE Transactions on Intelligent Transportation Systems
Vehicle–Vehicle Channel Models for the 5-GHz Band
IEEE Transactions on Intelligent Transportation Systems
Lane-Change Fuzzy Control in Autonomous Vehicles for the Overtaking Maneuver
IEEE Transactions on Intelligent Transportation Systems
IEEE Journal on Selected Areas in Communications
A Multiple-Goal Reinforcement Learning Method for Complex Vehicle Overtaking Maneuvers
IEEE Transactions on Intelligent Transportation Systems
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This paper proposes a novel eight-direction mechanism that can receive location information about nearby vehicles to perform self-analysis for lane-changing activities. In this work, we assume that each vehicle creates $$3\times 3$$ 3 脳 3 grids called safety-distance fields, and that the central grid is based on the given vehicle's location. Also assuming one on board unit in each vehicle, this work uses location information of nearby vehicles as input for a cellular automata (CA) model that calculates whether or not vehicles in nearby lanes are a safe distance from the vehicle for which the calculations are being performed. The current study evaluates this approach's performance by conducting computer simulations. Simulation results reveal the strengths of the proposed CA model in terms of increased safety distance and increased collision-avoidance for urban vehicular environments.