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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.

This paper proposes a method to quantify the driving risks of different traffic elements. We incorporate the objective analysis using naturalistic driving study (NDS) and the subjective analysis with driver attitude questionnaire (DAQ). The objective driving risks are investigated by a large-scale NDS experiment with multiple sources. Meanwhile, typical driver behavior parameters, such as velocity, time headway, and acceleration, are selected and analyzed. A self-reported survey of 364 drivers is conducted to subjectively evaluate the potential risks which drivers may suffer in various situations. NDS and DAQ are then combined together using the multinomial logit model to obtain the relative risks. Results demonstrate that the proposed method can provide an effective measure to quantify the influence factors of driving risks in dynamic environment. It is interesting to note that the risk value from subjective evaluation tends to be higher, implying that the subjective evaluation might be emotional and multi-factor coupling.