Driving Style

Driving style research focuses on understanding and modeling how individuals operate vehicles, aiming to improve the safety and comfort of both human-driven and autonomous vehicles. Current research emphasizes developing personalized driving models using machine learning techniques, including inverse reinforcement learning, neural networks (like Mixture Density Networks and LSTMs), and clustering methods, to capture the heterogeneity of driving behaviors and adapt vehicle control to individual preferences. This work is crucial for enhancing the acceptance and safety of autonomous vehicles by aligning their behavior with human expectations and for developing more effective driver assistance systems.