A Non-Intrusive Drowsiness Related Accident Prediction Model Based on D-S Evidence Theory

In this paper, a non-intrusive accident prediction model based on the information fusion technique of Dempster-Shafer (D-S) evidence theory is investigated, which can infer the belief probability of the drowsiness related accidents by integrating information from driver's eyes and driving performance. Firstly, to acquire the relevant physiological signals and driving performance data used in this proposed model, a driving simulator experiment is introduced. Secondly, three features reflecting driver drowsiness are quantitatively extracted, including gaze direction changing, blinking duration and standard lateral deviation. Finally, D-S evidence theory is applied to fuse these features. The inference results can predict over 70% drowsiness related accidents and therefore demonstrate the utility of this proposed model.