Trajectory Adaptive Prediction for Moving Objects in Uncertain Environment

TL;DR

This paper introduces an adaptive trajectory prediction method using a variation Gaussian mixture model that improves accuracy and efficiency in uncertain environments, suitable for mobile vehicle positioning.

Contribution

The paper presents a novel adaptive trajectory prediction approach based on VGMM with variational Bayesian inference and parameter adaptation for dynamic environments.

Findings

High predictive accuracy demonstrated in experiments

Maintains high time efficiency

Applicable to mobile vehicle positioning systems

Abstract

The existing methods for trajectory prediction are difficult to describe trajectory of moving objects in complex and uncertain environment accurately. In order to solve this problem, this paper proposes an adaptive trajectory prediction method for moving objects based on variation Gaussian mixture model (VGMM) in dynamic environment (ESATP). Firstly, based on the traditional mixture Gaussian model, we use the approximate variational Bayesian inference method to process the mixture Gaussian distribution in model training procedure. Secondly, variational Bayesian expectation maximization iterative is used to learn the model parameters and prior information is used to get a more precise prediction model. Finally, for the input trajectories, parameter adaptive selection algorithm is used automatically to adjust the combination of parameters. Experiment results perform that the ESATP method in the experiment showed high predictive accuracy, and maintain a high time efficiency. This model can be used in products of mobile vehicle positioning.