Abstract:

It is of great significance for the people-centered safe driving assistant system to realize the dynamic identification of driver’s propensity and vehicle type. Considering the privacy protection, a dynamic identification model for driver and vehicle characteristics was established using Bayesian decision tree. The ravel time obtained by global position system (GPS) data when cars went through an intersection was used to identify the vehicle type and driver’s propensity in the model. The identification effectiveness of human-vehicle characteristics under different permeability conditions was verified by real and virtual driving experiments. The results show that the accuracy rate of the established recognition model was above 80% and the established model was significantly better than the traditional decision tree model. The good consistency between the microscopic simulation considering driving tendency and the actual situation was verified by simulation experiment, and the rationality of the research results was proved indirectly.

Key words: vehicle safety, drivers and vehicles features, travel time, privacy protection, other-control technology, Bayesian decision tree