Human Driving Behavior

Understanding human driving behavior is crucial for improving road safety and developing effective autonomous driving systems. Current research focuses on modeling driver actions using diverse approaches, including hierarchical risk-aware planning frameworks, game-theoretic models incorporating driver risk preferences, and data-driven methods like behavioral cloning enhanced with environmental constraints and Gaussian process regression for prediction. These models aim to capture the complexity and variability of human driving, considering factors like driving style, situation awareness, and interactions with other vehicles. This research directly impacts the development and validation of autonomous vehicle technologies and contributes to a deeper understanding of human decision-making in dynamic environments.