Novel Vehicle Motion Model considering Driver Behavior for Trajectory Prediction and Driving Risk Detection

Abstract

Accurate prediction of vehicle motion status is critical for developing an advanced driver assistance system (ADAS), which can assess driving safety and detect dangerous scenarios in real time and in the near future. Although previous vehicle motion prediction models developed were mostly built on the basis of kinematic principles, driver behavior was largely ignored. Those models resulted in inaccurate trajectory predictions. To improve forecasting accuracy, the study reported here developed an improved vehicle motion model that includes consideration of both kinematic principles and real-time driver behavior. This improved vehicle motion model incorporates driver behavior into a constant acceleration (CA) model. Data on practical driver behavior, such as perception, identification, volition, and execution under traffic conditions and lane changes were collected. A quantitative approach based on a linear quadratic regulator optimal control method was used to acquire the driver's expected control input. In addition, a Kalman filter was applied to predict short-term vehicle motion, which was then used to analyze driving risks. Finally, CARSIM software was used to simulate driving scenarios. A Monte Carlo method was used to evaluate prediction accuracy and compare the results of the CA model and the improved vehicle motion model. The simulation results showed that the improved model can effectively simulate driver behavior in acceleration control by taking into full consideration the driver's volition and traffic environment. The proposed model yielded better predictions, provided an applicable way to improve the accuracy of vehicle motion prediction, and could be used to enhance the performance of ADAS.