Data Set Information:

原始数据是从一辆装有多个传感器的车辆上采集的，该车辆在德国南部的一个市区行驶了大约五个小时。传感器组包括一个单RGB摄像机、一个立体RGB摄像机、一个带差分GPS的惯性测量系统和一个激光雷达系统。该存储库提供的预处理数据包括45条人行道（世界坐标）以及静态环境的语义地图。对于每个轨道和每个时间步，不仅提供了代理位置，还提供了身体和头部方向属性，以及所有其他代理的位置及其类型（如汽车、自行车、行人等）。

Attribute Information:

: Pedestrian tracks are stored in the tracks.csv. Each row in such files contains 14 comma-separated attributes, with missing values denoted by a€?Nonea€?. The attributes are in order:
a€￠ oid: unique agent id (int),
a€￠ timestamp: time in seconds (float),
a€￠ x: x component of position vector (float),
a€￠ y: y component of position vector (float),
a€￠ body_roll: roll body angle in degrees (float),
a€￠ body_pitch: pitch body angle in degrees (float),
a€￠ body_yaw: yaw body angle in degrees (float),
a€￠ head_roll: roll head angle in degrees (float),
a€￠ head_pitch: pitch head angle in degrees (float),
a€￠ head_yaw: yaw head angle in degrees (float),
a€￠ other_oid: list of ids of agents currently present in the scene ([list of int]),
a€￠ other_class: list of other agentsa€? class labels ([list of int]),
a€￠ other_x: list of other agentsa€? x coordinates ([list of float]),
a€￠ other_y: list of other agentsa€? y coordinates ([list of float]).
Labels used to identify agent types are available in agent_class_label_info.csv.
The file semantic_map.png contains a map of the static environment, where semantic labels are color-encoded according to the mapping available in semantic_map_label_info.csv. Information needed to transform between image and world coordinates is stored in the file map2world_info.txt.

Relevant Papers:

[1] Blaiotta, Claudia. 'Learning generative socially-aware models of pedestrian motion.' IEEE Robotics and Automation Letters, 2019.

Citation Request:

You may use this data for scientific, non-commercial purposes, as long as you give credit to the owners when publishing any work based on this data. Please cite [1].