An Enhanced Fuzzy Logic-Particle Swarm Optimization Algorithm for the Strategy Control of Self-driving Electric Vehicles
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With the current rapid advancement of science and technology, there is an increasing focus on comprehensive research and the development of practical solutions for self-driving electric cars to address challenges, including environmental pollution, renewable energy utilization, emission control, and battery recycling. In this study, automatic direction control is achieved for electric vehicles by implementing line-tracing autonomous vehicles equipped with computer vision-based cameras, utilizing Particle Swarm Optimization (PSO), the Takagi–Sugeno Fuzzy model, and the PID control system. Line-tracing autonomous vehicles are devices capable of recognizing and tracking black or painted lines on the road. The lines are designed to be easily recognizable with a clear contrast, such as a white line on a black background. The autonomous vehicle follows a distinct, marked line to guide its journey. In this study, we integrate computer vision techniques with Particle Swarm Optimization (PSO) and a Takagi–Sugeno fuzzy control system for automatic direction control. Additionally, the speed and turning direction of the electric vehicle are regulated by a controller that combines proportional, integral, and derivative (PID) stages. According to real-world experiments with road-following autonomous vehicles using camera image processing, the highest success rate of 99.8% is achieved when the car employs intelligent algorithms to navigate turns of 10, 20, 30, and 40 degrees. Likewise, tests have demonstrated that the electric vehicle can achieve a perfect success rate of 100% when driving on a straight road.
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