复杂障碍空间中基于移动对象运动规律的不确定轨迹预测

现有移动对象的轨迹预测大部分是针对路网空间,然而在实际地理环境中往往存在障碍物,移动对象的运动基本在障碍空间中进行。近年来,已有较多关于路网空间中移动对象轨迹预测的研究以及障碍空间中障碍范围查询、最近邻查询等的研究,但是目前尚没有障碍空间中移动对象不确定轨迹预测的相关研究。为此,提出障碍空间中基于移动对象运动规律的不确定轨迹预测方法。首先,利用障碍物之间的区域关系对障碍空间进行剪枝；其次,提出障碍空间期望距离概念,对障碍空间的轨迹数据进行轨迹聚类,从而挖掘移动对象的热点区域；然后,根据各热点区域间的障碍距离和历史访问习惯得到转移的综合概率,提出基于移动对象运动规律的轨迹预测算法；最后,通过实验验证了算法的准确性和高效性。

Prediction of Uncertain Trajectory Based on Moving Object Motion in Complex Obstacle Space

Most of the existing moving objects trajectory prediction is in the road network space,however,in the actual geographical environment,there exists obstacles,the movement of moving objects is basically carried out in the obstacle space.In recent years,there have been many studies on moving object trajectory prediction in road network space,such as obstacle range query,nearest neighbor query and so on.However,there is no research on the uncertain trajectory prediction of moving objects in obstacle space.For this reason,this paper proposed an uncertain trajectory prediction algorithm based on moving object motion in obstacle space.Firstly ,the obstacle space was pruned by using the regional relation among obstacles.Secondly,the concept of obstacle space expectation distance was proposed,and the trajectory data of obstacle space is clustered,thereby excavating the moving object hot spot region.Next,according to the obstacle distance and the historical visiting habit of each hotspot region,a Markov trajectory prediction algorithm based on the motion law was proposed.Finally,the accuracy and efficiency of the algorithm were verified by experiments.