A Novel AI-based Approach for the Design of VANET Communication Protocol

Vehicular ad-hoc network (VANET) corresponds to a transportation system in which the communication among the vehicles and with the roadside base units can be completed smartly and hence acknowledged as a smart transportation system. Fuzzy logic is an AI method in which the decision is made depending upon the standard truth values instead of true/false or 0/1 values like Boolean Algebra. Thus, Fuzzy logic in VANETs (Vehicular Ad Hoc Networks) can be a valuable tool for handling uncertainty and imprecision in decision-making processes, particularly in dynamic and complex traffic environments. Fuzzy logic can be used to regulate traffic flow at intersections or merge points depending on real-time traffic density, vehicle speed, and queue length, optimizing traffic flow while minimizing congestion. Fuzzy logic controllers can help vehicles make decisions regarding speed adjustment, lane change, or braking to avoid collisions. Inputs such as distance to other vehicles, relative speed, and road conditions can be fuzzified to determine the appropriate action. In this paper, we proposed a fuzzy logic AI technology used in VANETs to avoid heavy traffic and collision issues at the intersection of the road. The results show that fuzzy logic can assist in adaptive route planning by considering factors such as traffic congestion, road conditions, and user preferences. Fuzzy inference can generate route recommendations based on these inputs, balancing travel time, fuel efficiency, and safety.