Mobile Agent Trajectory Prediction using Bayesian Nonparametric Reachability Trees
Mobile Agent Trajectory Prediction using Bayesian Nonparametric Reachability Trees
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DOI：
10.2514/6.2011-1512
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发表时间：
2011-03
期刊：
影响因子：
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通讯作者：
Georges Aoude;J. Joseph;N. Roy;J. How
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文献类型：
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作者：
Georges Aoude;J. Joseph;N. Roy;J. How
本文提出了一种有效的轨迹预测算法，已发展到提高未来的防撞和检测系统的性能。其主要思想是将周围代理的推断意图信息嵌入到其估计的可达集合中，以获得其未来路径的概率描述。更具体地说，所提出的方法结合了最近开发的RRT到达算法和高斯过程的混合物。RRT-Reach算法是作者对闭环快速探索随机树算法（CL-RRT）的扩展，用于实时计算移动对象的可达集。混合高斯过程（GP）是一种灵活的非参数贝叶斯模型，用于表示轨迹上的分布，并已在无人机拦截和跟踪地面车辆的规划方案的作者以前证明。混合物使用从统计数据中学习的典型机动进行训练，并且RRT-Reach利用来自GP的样本来增长周围车辆的概率加权可行路径。由此产生的方法，表示为RR-GP，具有RRTReach的计算轨迹的好处，是动态可行的建设，因此有效地近似周围车辆的可达性集以下的典型模式。RRT-GP还具有GP混合物的优点，即对RRTReach产生的可行轨迹提供概率加权，使我们的系统能够通过其可能性系统地加权轨迹。一个示范性的例子，汽车一样的车辆说明了RR-GP方法的优点，通过比较它与其他两个基于GP的算法。
This paper presents an efficient trajectory prediction algorithm that has been developed to improve the performance of future collision avoidance and detection systems. The main idea is to embed the inferred intention information of surrounding agents into their estimated reachability sets to obtain a probabilistic description of their future paths. More specifically, the proposed approach combines the recently developed RRT-Reach algorithm and mixtures of Gaussian Processes. RRT-Reach was introduced by the authors as an extension of the closed-loop rapidly-exploring random tree (CL-RRT) algorithm to compute reachable sets of moving objects in real-time. A mixture of Gaussian processes (GP) is a flexible nonparametric Bayesian model used to represent a distribution over trajectories and have been previously demonstrated by the authors in a UAV interception and tracking of ground vehicles planning scheme. The mixture is trained using typical maneuvers learned from statistical data, and RRT-Reach utilizes samples from the GP to grow probabilistically weighted feasible paths of the surrounding vehicles. The resulting approach, denoted as RR-GP, has RRTReach’s benefits of computing trajectories that are dynamically feasible by construction, therefore efficiently approximating the reachability set of surrounding vehicles following typical patterns. RRT-GP also features the GP mixture’s benefits of providing a probabilistic weighting on the feasible trajectories produced by RRTReach, allowing our system to systematically weight trajectories by their likelihood. A demonstrative example on a car-like vehicle illustrates the advantages of the RR-GP approach by comparing it to two other GP-based algorithms.