Imagine teaching a self-driving car the way you'd teach a teenager – not with rigid rules, but through real-world experience and gentle guidance. That’s the essence of a groundbreaking new approach to autonomous driving emerging from Rutgers University. Traditional self-driving systems struggle to replicate the nuanced decision-making of human drivers. Current AI models often rely on Reinforcement Learning (RL), rewarding the AI for avoiding crashes but overlooking the subtle aspects of safe, comfortable driving. This new research aims to bridge that gap using a technique called Reinforcement Learning from Human Feedback (RLHF), combined with the power of Large Language Models (LLMs). The researchers have created a realistic driving simulation environment where human drivers navigate alongside AI-controlled cars and pedestrians. This setup captures not only the drivers’ actions (steering, braking, etc.) but also their physiological responses (heart rate, skin conductivity) and visual focus. The magic happens when all this data is fed into a Large Language Model. The LLM acts as an interpreter, translating human reactions into a language the AI car can understand. For example, a sudden increase in heart rate during a sharp turn tells the LLM that the maneuver was uncomfortable. This feedback helps refine the AI's driving style, making it smoother and more human-like. This research goes beyond just avoiding collisions; it's about creating a driving experience that feels natural and safe for everyone on the road. The next phase of research involves real-world testing in New Jersey and New York City, gathering data that will further enhance the AI’s adaptability and safety. While still in its early stages, this research holds immense potential for the future of self-driving cars, offering a path towards safer, more human-centered autonomous driving.