Optimization and Implementation of Fuzzy Logic Controllers for Precise Path Tracking in Autonomous Driving

Abstract

The optimization and implementation of Fuzzy Logic Controllers (FLCs) for precise path tracking in autonomous driving is an intricate and highly technical endeavor. This research focuses on understanding the core principles, applications, optimization techniques, implementation considerations, and validation methods related to FLCs in autonomous driving. Beginning with an introduction to FLCs, we delve into the multi-valued logic that provides an adaptive, human-like decision-making process. Within the scope of autonomous driving, path tracking stands out as a critical task requiring the continuous fine-tuning of steering, throttle, and brake. FLCs offer a solution to this, providing adaptive control through the use of fuzzy rules and membership functions, accommodating various road conditions and driving scenarios. The optimization techniques of Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) are explored for tuning and enhancing the FLCs, thereby augmenting their performance and adaptability. On the implementation front, real-time processing considerations are emphasized, including code optimization and suitable hardware selection, along with the integration of the FLC with other systems such as sensors, actuators, and navigation units. Safety is also addressed, highlighting the necessity for robust mechanisms to manage unexpected situations and failures within the control system. Finally, the abstract discusses the vital role of extensive simulation and field testing using real-world scenarios, all aiming to validate the performance of the optimized FLC in various driving conditions. The exploration of hybrid approaches that combine fuzzy logic with other intelligent techniques, such as neural networks, is also hinted at, suggesting a pathway to even more advanced and adaptive control systems for autonomous vehicles.