Naturalistic Driving

Naturalistic driving research focuses on understanding and modeling real-world driving behavior to improve the safety and efficiency of both human drivers and autonomous vehicles. Current research emphasizes developing data-driven models, often employing machine learning techniques like graph neural networks, reinforcement learning, and hidden Markov models, to analyze large naturalistic driving datasets and generate realistic, safety-critical scenarios for testing autonomous systems. This work is crucial for advancing the development and validation of advanced driver-assistance systems and autonomous vehicles, ultimately contributing to safer and more efficient transportation systems.