ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2017, Vol. 54 ›› Issue (11): 2434-2444.doi: 10.7544/issn1000-1239.2017.20170309

Special Issue: 2017车联网关键技术与应用研究专题

Previous Articles     Next Articles

Adaptive Trajectory Prediction for Moving Objects in Uncertain Environment

Xia Zhuoqun1,2,3, Hu Zhenzhen1,2, Luo Junpeng1,2, Chen Yueyue3   

  1. 1(Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation (Changsha University of Science and Technology), Changsha 410114); 2(School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114); 3(College of Computer, National University of Defense Technology, Changsha 410114)
  • Online:2017-11-01

Abstract: The existing methods for trajectory prediction are difficult to describe the trajectory of moving objects in complex and uncertain environment accurately. In order to solve this problem, this paper proposes an self-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. This algorithm can take a priory information. Finally, for the input trajectories, parameter adaptive selection algorithm is used automatically to adjust the combination of parameters, including the number of Gaussian mixture components and the length of segment. Experimental results perform that the ESATP method in the experiment shows high predictive accuracy, and maintains a high time efficiency. This model can be used in products of mobile vehicle positioning.

Key words: environment adaptive, variational Gaussian mixture model (VGMM), parameter adaptive selection algorithm, trajectory prediction

CLC Number: