Simulation and Control of a Vehicle on Virtual Road Using Fuzzy Logic

This article presents a simulation model for vehicle navigation and control. The model simulates a vehicle's movement on a virtual road while employing fuzzy logic to control its trajectory. Fuzzy logic, a widely-used artificial intelligence technique, excels at handling uncertainty and ambiguity while making decisions in complex environments. In this implementation, we utilize fuzzy logic controllers to regulate both vehicle acceleration and steering mechanisms, enabling safe road navigation and collision avoidance. From a code implementation perspective, the model typically involves: - A vehicle dynamics module simulating kinematic/dynamic properties - Sensor input processing for environmental awareness - Fuzzy inference systems with membership functions for acceleration and steering control - Rule-based decision making using IF-THEN statements Key algorithmic components include: 1. Fuzzification of inputs (position, velocity, obstacle distance) 2. Fuzzy rule evaluation using min-max operations 3. Defuzzification techniques (e.g., centroid method) to produce crisp control outputs This model finds applications in autonomous vehicle development, traffic flow simulation, and intelligent transportation systems. Overall, it serves as a valuable tool for understanding vehicle control principles and has significant practical implications in real-world implementations.