基于目的地的轨迹建模与CM序列
CM Sequence based Trajectory Modeling with Destination
AI总结：
本文提出利用CM_L序列建模具有目的地导向的轨迹，通过引入联合端点密度和马尔可夫-like演化法则，改进传统马尔可夫序列在建模轨迹终点方面的不足，并通过仿真展示其在轨迹建模与预测中的有效性。
摘要：
在某些问题中，移动物体的目的地信息是已知的。例如，一架飞机从起点飞往目的地。此类问题有三个主要组成部分：起点、终点以及中间的运动。为了强调轨迹最终到达目的地，我们将它们称为 extit{目的地导向轨迹}。马尔可夫序列不足以建模此类轨迹。给定一个初始密度和演化法则，马尔可夫序列的未来是概率确定的。一类条件马尔可夫（CM）序列，称为$CM_L$序列（包括马尔可夫序列作为特殊情况），具有以下主要组成部分：联合端点密度（即初始密度和在初始条件下最终密度）和马尔可夫-like演化法则。本文提出利用$CM_L$序列来建模目的地导向轨迹。展示了$CM_L$序列在目的地导向轨迹建模中具有若干有利性质。一些轨迹建模与预测的仿真结果用于说明。
英文摘要：
In some problems there is information about the destination of a moving object. An example is an airliner flying from an origin to a destination. Such problems have three main components: an origin, a destination, and motion in between. To emphasize that the motion trajectories end up at the destination, we call them \textit{destination-directed trajectories}. The Markov sequence is not flexible enough to model such trajectories. Given an initial density and an evolution law, the future of a Markov sequence is determined probabilistically. One class of conditionally Markov (CM) sequences, called the $CM_L$ sequence (including the Markov sequence as a special case), has the following main components: a joint endpoint density (i.e., an initial density and a final density conditioned on the initial) and a Markov-like evolution law. This paper proposes using the $CM_L$ sequence for modeling destination-directed trajectories. It is demonstrated how the $CM_L$ sequence enjoys several desirable properties for destination-directed trajectory modeling. Some simulations of trajectory modeling and prediction are presented for illustration.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
