Abstract
This chapter presents a comprehensive study on the integration of neural networks and fuzzy logic control techniques for enhancing the autonomy of electric vehicles (EVs). The integration of these two paradigms aims to overcome the limitations of traditional control approaches by leveraging the complementary strengths of neural networks in learning complex patterns and fuzzy logic in handling uncertainty and imprecision. The chapter discusses the design, implementation, and evaluation of an autonomous EV control system that utilizes neural networks for learning vehicle dynamics and fuzzy logic for decision-making in various driving scenarios. Through extensive simulations and experiments, the effectiveness and robustness of the proposed integrated approach are demonstrated, showcasing its potential for improving the safety, efficiency, and adaptability of autonomous EVs in real-world environments.
Published Version
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