Driver Model

Driver models aim to replicate human driving behavior for applications in advanced driver-assistance systems (ADAS), automated driving system (ADS) testing, and traffic simulation. Current research emphasizes personalized models that incorporate individual driver characteristics like risk perception and attention, often leveraging machine learning techniques such as deep reinforcement learning and federated learning to improve accuracy and adapt to diverse driving styles. These advancements are crucial for enhancing the safety and reliability of ADAS and ADS, as well as for improving traffic flow modeling and the development of more effective driver training and assistance tools.