Integration of Neuro-Fuzzy Control in an Autonomous Driving System: A Hybrid Approach

This paper introduces a novel hybrid control strategy for autonomous driving that integrates neurotechnology-inspired signal modeling with conventional PID control to enhance vehicle performance and adaptability. The proposed approach combines a standard, fixed-gain PID controller with a dynamic neuro-fuzzy system that adjusts controller gains in real time via a dual-layer fuzzy logic framework. Specifically, a 'Fuzzy Brain' module estimates the driver's Emotive State from surrogate inputs - such as lateral acceleration and yaw frequency ratios - while a 'Fuzzy PID' module modulates the PID gains accordingly. Vehicle dynamics are modeled using the widely adopted bicycle model, and numerical simulations are conducted in a cone slalom scenario at speeds of 50,75, and 100 km h. Results indicate that under high-demand conditions, the hybrid system yields improved trajectory tracking, stability, and responsiveness. Moreover, when the Emotive State is relaxed, the system prioritizes comfort by adopting less aggressive gains, thereby providing a balanced trade-off between agility and ride quality. These findings demonstrate that incorporating cognitively inspired inputs into autonomous control architectures can pave the way for safer, more adaptable driving systems.