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