A Driving Risk Assessment Framework Considering Driver’s Fatigue State and Distraction Behavior

Fatigue and distraction are the most common long-term poor state and short-term abnormal behavior of drivers, significantly increasing the driving risk of vehicles equipped with the advanced driver assistance system (ADAS). To provide a more reliable decision-making basis for ADAS and improve driving safety, this paper proposes a driving risk assessment framework considering the driver’s long-term poor state and short-term abnormal behavior. Firstly, based on the self-built fatigue dataset and transfer learning method, an adaptive fatigue detection model with strong generalization capability is established to enable multi-view driver fatigue detection. Then, the idea of multi-clustering and adding offset parameters is introduced into the classical contrast loss function, and the D-InfoNCE loss function is designed to realize the accurate identification of the driver’s specific distraction behavior under open set detection. Subsequently, a driving risk assessment system is developed to quantify driving risk based on the vehicle driving risk factors when fatigued or distracted driving occurs. Finally, the proposed driving risk assessment system is validated by the datasets and driver-in-the-loop test bench. The results show that the proposed framework can accurately detect the driver’s fatigue state and distraction behavior and give ADAS the corresponding driving risk levels to enhance driving safety.