Research on driver posture recognition and prediction in vehicle

Abstract

The statistics show that more than 90% of traffic accidents are caused by the individual factors of the driver. Therefore, real-time monitoring of the current driver’s posture in the car and further predicting of the future driving posture are of great significance for designing the Adaptive Restraint System (ARS) to improve the safety protection of drivers. In this study, a real-time vehicle driver posture recognition and prediction method is proposed. The traditional Kinect-OpenPose algorithm, which has the problems such as lack of depth value, key point pulsation and measurement error of Kinect, was optimized. The OpenPose network structure and skeleton model are modified to improve the algorithm. In this study, a key point motion trajectory prediction network based on Long Short Term Memory (LSTM) is developed. The hyperparameter optimization method is combined with multi-layer grid search to obtain the best prediction effect in braking and steering conditions, and the driver’s future posture can be obtained by predicting the subsequent movement trajectory of key points. It has been verified that the method in this study has high accuracy, high robustness and low cost in key point recognition and prediction. And the improved processing speed of the algorithm did not affect the accuracy of driving posture recognition and prediction. This method predicted the driver’s posture in future of the collision, which can be used to guide the development of controlling system of the ARS.