通过轨迹特征量化区分行人与车辆的结构化与非结构化环境。
Characterizing Structured versus Unstructured Environments based on Pedestrians' and Vehicles' Motion Trajectories
- 提取速度、轨迹变异等特征，用聚类与模型区分环境类型。
- 非结构化环境中行人密度高、停顿率高、轨迹更不规则。
- 为自动驾驶轨迹预测提供可量化的环境分类依据。
行人与车辆在接近运行时，在非结构化环境与结构化环境中的轨迹行为存在差异。这些差异对自动驾驶车辆的轨迹预测算法具有重要价值。然而，当前用于轨迹预测基准测试的行人与车辆轨迹数据集并未按环境类型进行分类。同时，现有对结构化与非结构化环境的定义多为定性描述，难以准确判断具体环境类型。本文通过分析多个现有数据集，提取均值速度、轨迹变异等轨迹特征，结合K-means聚类与广义线性模型，提出更定量的环境区分方法。结果表明，轨迹变异度、停顿比例及行人密度在两类环境中存在显著差异，可用于数据集的环境类型分类。
原文摘要 · Abstract (English)
Trajectory behaviours of pedestrians and vehicles operating close to each other can be different in unstructured compared to structured environments. These differences in the motion behaviour are valuable to be considered in the trajectory prediction algorithm of an autonomous vehicle. However, the available datasets on pedestrians' and vehicles' trajectories that are commonly used as benchmarks for trajectory prediction have not been classified based on the nature of their environment. On the other hand, the definitions provided for unstructured and structured environments are rather qualitative and hard to be used for justifying the type of a given environment. In this paper, we have compared different existing datasets based on a couple of extracted trajectory features, such as mean speed and trajectory variability. Through K-means clustering and generalized linear models, we propose more quantitative measures for distinguishing the two different types of environments. Our results show that features such as trajectory variability, stop fraction and density of pedestrians are different among the two environmental types and can be used to classify the existing datasets.
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