Fusion of Machine Learning and Fuzzy Logic for Intelligent Control Systems in Autonomous Vehicles

The increasing complexity of autonomous vehicle operations necessitates intelligent control
systems that can effectively handle uncertainty, non-linearity, and dynamic multi-task scenarios in real
time. This book chapter presents a comprehensive exploration of hybrid control architectures that fuse
machine learning with fuzzy logic to enhance decision-making in autonomous driving applications.
The synergy between data-driven adaptability and rule-based interpretability enables these hybrid
systems to manage complex environments while maintaining transparency and robustness. The chapter
systematically addresses the taxonomy of neuro-fuzzy systems, fuzzy-Q learning, and deep fuzzy
networks, emphasizing their adaptability across diverse driving tasks. It examines the integration of
sensor fusion, contextual reasoning, and explainable AI into hybrid control loops, along with
deployment strategies using embedded middleware frameworks such as ROS and AUTOSAR. Case
studies in both urban and highway environments illustrate the performance of hybrid architectures in
managing concurrent control objectives under real-world constraints. By unifying theoretical
advancements with practical implementations, this chapter contributes to the advancement of safe,
explainable, and adaptive autonomous vehicle control systems.