For intelligent transportation systems, digital military battlefield and driver assistance systems, it is of great practical value to predict the trajectories of moving objects with uncertainty in a real-time, accurate and reliable fashion. Intelligent trajectory prediction can not only provide accurate location-based services, but also monitor and estimate traffic to suggest the best path, and as such becomes an active research direction. Aiming to overcome the drawbacks of the existing methods, a new trajectory prediction model based on Gaussian mixture models called GMTP is proposed. The new model contains the following essential phases: (1) modeling the complex motion patterns based on Gaussian mixture models, (2) calculating the probability distribution of different types of motion patterns by using Gaussian mixture model in order to partition trajectory data into distinct components, and (3) inferring the most possible trajectories of moving objects via Gaussian process regression. The GMTP algorithm is naturally a Gaussian nonlinear statistical probability model and the advantage of the proposed model is that the result is not only a predicted value, but also a whole distribution beyond the future trajectories, therefore making it possible to infer the location in regard to some motion patterns, e.g., uniformly accelerated motion, by using statistical probability distribution. Extensive experiments are conducted on real trajectory data sets and the results show that the prediction accuracy of the GMTP algorithm is improved by 22.2% and 23.8%, and the runtime can be reduced by 92.7% and 95.9% on average, respectively, when compared to the Gaussian process regression model and Kalman filter prediction algorithm with similar parameter setting.%在智能交通控制系统、军事数字化战场、辅助驾驶系统中,实时、精确、可靠的移动对象不确定性轨迹预测具有极高的应用价值.智能轨迹预测不仅可以提供精准的基于位置的服务,而且可以提前监测和预判交通状况,进而推荐最佳路线,已经成为移动对象数据库研究的热点,亟需设计准确而高效的位置预测方法.针对现有方法的不足,提出了基于高斯混合模型的轨迹预测方法 GMTP,主要步骤包括:(1) 针对复杂运动模式利用高斯混合模型建模;(2) 利用高斯混合模型计算不同运动模式的概率分布,进而将轨迹数据划分为不同分量;(3) 利用高斯过程回归预测移动对象最可能的运动轨迹.GMTP 是高斯非线性概率统计模型,其优势在于:计算结果不仅是位置预测值,更是关于移动对象未来所有可能运动轨迹的概率分布,可以利用概率统计分布特性获得某种运动模式(如匀加速运动)下的位置预测.大量真实轨迹数据集上的实验结果表明:与相同参数设置下的高斯回归预测和卡尔曼滤波预测法相比,GMTP的预测准确性平均提高了22.2%和23.8%,预测时间平均缩减了92.7%和95.9%.
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