Adaptive detection method for driver fatigue using facial multisource dynamic behavior fusion

Driving while fatigued is a leading cause of traffic accidents. This study proposed an adaptive detection model to recognize driver fatigue based on the dynamic facial behavior information of drivers. First, drivers’ facial fatigue features were extracted to establish a general feature space, including pupil movement, eye state, and fatigue expression parameters. A differentiated feature space was then built based on individual drivers, taking into account the homogeneity, regularity, and individual variances in drivers' facial behavior at various states. A complete adaptive fatigue feature space was built by integrating the general feature space and differentiated feature space. Finally, a driver adaptive fatigue discrimination model was constructed to classify the general and adaptive fatigue feature space to detect driver fatigue states adaptively. A driver fatigue detection dataset from real scenarios had been established to validate the performance of the proposed model. Experimental results demonstrated that the proposed method significantly improved the detection accuracy of driver fatigue. In terms of artificial intelligence, this study contributes a novel adaptive feature space construction method based on multimodal dynamic feature fusion for facial fatigue recognition; in engineering application, it develops an adaptive driver fatigue detection system grounded in multimodal dynamic behaviors, which provides real-time alerts upon detecting driver fatigue and ensures driving safety.