The implementation of road user models that realistically reproduce a credible behavior in a multi-agentsimulation is still an open problem. A data-driven approach consists on to deduce behaviors that may exist in real situation to obtain different types of trajectories from a large set of observations. The data, and its classification, could then be used to train models capable to extrapolate such behavior. Cars and two different types of Vulnerable Road Users (VRU) will be considered by the trajectory clustering methods proposed: pedestrians and cyclists. The results reported here evaluate methods to extract well-defined trajectory classes from raw data without the use of map information while also separating ''eccentric'' or incomplete trajectories from the ones that are complete and representative in any scenario. Two environments will serve as test for the methods develop, three different intersections and one roundabout. The resulting clusters of trajectories can then be used for prediction or learning tasks or discarded if it is composed by outliers.

使用多智能体仿真实现具有可信行为的道路用户模型的问题尚未解决。数据驱动方法是通过推断在真实情况中可能存在的行为，从大量观察中获取不同类型的轨迹，并使用其分类来训练能够推测这种行为的模型。该研究在考虑车辆和两种不同类型的弱势道路用户的基础上，提出了轨迹聚类方法，并评估了从原始数据中提取明确定义的轨迹类别的方法，同时将“离奇”或不完整的轨迹与在任何场景下都具有代表性和完整的轨迹分离开来。然后通过三个不同的交叉口和一个环形交叉口作为测试环境，使用开发的方法进行测试。所得到的轨迹簇可以用于预测或学习任务，或者如果由离群值组成则可以舍弃。

航空观测中的快速机动恢复：轨迹聚类与异常点剔除