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.

翻译：在多智能体仿真中实现能够真实再现可信行为的道路使用者模型仍是一个开放性问题。一种数据驱动方法是从大量观测数据中推断现实场景中可能存在的行为，从而获得不同类型的轨迹。这些数据及其分类结果可用于训练能够外推此类行为的模型。本文提出的轨迹聚类方法将考虑汽车和两类弱势道路使用者：行人与骑行者。本文报告的结果评估了从原始数据中提取明确定义的轨迹类别的方法，这些方法无需使用地图信息，同时能将"异常"或不完整轨迹与任何场景中完整且具有代表性的轨迹分离。所开发的方法将在两种环境中进行测试：三个不同的交叉路口和一个环岛。最终得到的轨迹聚类结果可用于预测或学习任务，若由异常值构成则可予以剔除。