UPTP Vehicle Trajectory Prediction Based on User Preference Under Complexity Environment

Accurate and reliable vehicle trajectory prediction is a vital component of any intelligent transportation system, which can improve the traffic safety and facilitate effective urban road planning. Because vehicle trajectories are uncertain and are characterized by vulnerability to environmental and user behaviors, they are exceedingly difficult to accurate via modern trajectory prediction technology. This paper proposes a vehicle trajectory prediction method that integrates environmental awareness and user preferences. First,the disutility function value is applied to the logarithmic model to calculate the user path selection probability. Secondly, the SSEM algorithm is used to process the user preferences and obtain a user type distribution. Finally, the optimal variational Gaussian mixture model is used to represent the complex environment, and the obtained user-type distribution is used to implement on-line prediction. The results of a comprehensive evaluation experiment indicate that the proposed method is more accurate than other existing prediction methods.

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