基于行人与车辆运动轨迹的结构化与非结构化环境特征描述
Characterizing Structured versus Unstructured Environments based on Pedestrians' and Vehicles' Motion Trajectories
AI总结：
本文通过提取轨迹特征并运用K-means聚类和广义线性模型，提出量化指标以区分结构化与非结构化环境，发现轨迹变异性、停车比例和行人密度可用于分类现有数据集。
摘要：
在非结构化环境中，近距离运行的行人和车辆的运动轨迹行为可能与结构化环境中的不同。这些运动行为的差异对于自动驾驶车辆的轨迹预测算法而言具有重要的参考价值。然而，目前常用的作为轨迹预测基准的行人和车辆轨迹数据集，并未根据其环境性质进行分类。另一方面，现有对非结构化与结构化环境的定义较为定性，难以用于判定特定环境类型。本文基于提取的若干轨迹特征（如平均速度和轨迹变异性）对多个现有数据集进行了比较。通过K-means聚类和广义线性模型，我们提出了更定量的度量方法来区分这两种不同类型的环境。我们的结果表明，轨迹变异性、停车比例和行人密度等特征在这两类环境之间存在差异，可用于对现有数据集进行分类。
英文摘要：
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.
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。
