An Integrated Approach to Probabilistic Vehicle Trajectory Prediction via Driver Characteristic and Intention Estimation

Abstract

Probabilistic trajectory prediction for other vehicles can be an effective way to improve the understanding of dynamic and stochastic traffic environment for automated vehicles. One challenge is how to predict the vehicle trajectory accurately both in the short-term and long-term horizon. In this paper, we propose an integrated approach combining the driver characteristic and intention estimation (DCIE) model with the Gaussian process (GP) model. Our proposed method makes use of both vehicle low-level and high-level information and inquires parameters by learning from public naturalistic driving dataset. Our method is applied and analyzed in the highway lane change scenarios. Compared with other traditional methods, the advantages of this proposed method are demonstrated by more accurate prediction and more reasonable uncertainty description during the whole prediction horizon.