An integrated active-passive safety strategy for automobiles based on driver state recognition and injury risk prediction.

This study proposes an integrated active-passive safety strategy based on driver state recognition and injury risk prediction, aiming to enhance vehicle safety by dynamically coordinating the operation of the autonomous emergency braking (AEB) system and occupant restraint systems. First, injury prediction and driver state recognition models were developed using machine learning and deep learning techniques, respectively, based on real-world traffic accident data and physiological signals. These predictive outcomes were then incorporated into a fuzzy control algorithm to optimize the AEB system, enabling it to dynamically adjust activation timing according to varying driver states and potential injury risks. Experimental results demonstrate that the optimized AEB system effectively adapts braking initiation based on driver responsiveness and injury severity, significantly improving collision avoidance performance. Furthermore, by integrating passive safety mechanisms, the control parameters of seatbelts and airbags were optimized, resulting in a 30.60% reduction in the head injury criterion (HIC) and a 22.44% decrease in the weighted injury criterion (WIC). This study provides novel insights and methodological approaches for the integrated optimization of intelligent vehicle safety systems, offering both theoretical and practical value.