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.
翻译:现有的轨迹预测方法很难准确描述在复杂和不确定的环境中移动物体的轨迹。为了解决这一问题,本文件提议了一种根据动态环境中的变异高斯混合模型(VGMM)移动物体的适应性轨迹预测方法。首先,根据传统的混合高斯模型,我们使用近似变异贝叶斯推论方法处理模型培训程序中的混合物高斯分布。第二,使用变异巴伊西亚预期最大化迭代来学习模型参数,并使用先前的信息获得更精确的预测模型。最后,对于输入轨迹,参数适应性选择算法被自动用于调整参数组合。实验结果显示,实验中的ESATP方法显示高预测性精度,并保持高时间效率。这一模型可用于移动车辆定位产品。