Hybrid Fuzzy Neural Systems for Real Time Decision Making in Autonomous Vehicles

The real-time decision making for autonomous vehicles is challenging because the driving environment is high-dimensional, dynamic, and uncertain. One such approach that shows promise is the use of hybrid fuzzy-neural systems which capitalize on the human-like reasoning of fuzzy logic combined with the adaptive learning capabilities of the neural network. In this paper, we will study some of the such systems developed and utilized for improving decision-making in autonomous vehicles. The proposed method employs fuzzy logic to process vague or imprecise data, allowing the system to function in the lack of crisp data or in uncertain situations. At the same time, we have neural networks, which learn from the big data, figure out what is best to do in a variety of situations by gaining experience and improving accuracy for their decisions as time goes on. The hybrid system, by integrating both the model-based and data-driven approaches, is capable of handling complex and dynamic inputs such as variations in traffic, human walking patterns, and sudden obstacles, resulting in more accurate and reliable decision-making in a timely manner. Experimental evaluations show that H-FN AMURs achieve significantly better navigation accuracy and responsiveness than AMURs based merely on fuzzy logic or neural network models. Combining these systems enables adaptive learning and strong decision-making, necessary for living in an unpredictable environment and assuring passenger safety. Through a well-designed framework, this study addresses the question on how intelligent transportation systems can improve the decision-making processes of autonomous vehicles. Further, a final address will explore the potential of integrating driving-by-weight simulations to create efficient hybrid models for such autonomous navigation.