VRU Trajectory Dataset

VRUT_Dataset_complete.tar.gz : The VRU Trajectory Dataset consists of 1068 pedestrian and 464 cyclist trajectories recorded at an urban intersection using cameras and LiDARs. A detailed description of the intersection can be found in [1]. The pedestrian trajectories were recorded by using a wide angle stereo camera system to track the pedestrians' head position and generating the 3D position by triangulation. The cyclists trajectories were recorded by using LiDARs to track the center of gravity of the cyclists. The cameras operate at 50 Hz, the LiDARs at 12.5 Hz. The dataset partly results from the projects DeCoInt² [2] funded by the "German Reasearch Foundation" (DFG) and AFUSS funded by the "Bundesministerium für Bildung und Forschung" (BMBF). Additionally, our work is supported by "Zentrum Digitalisierung Bayern". Dataset Format: The complete dataset consists of 1532 files in csv format, where every file contains one VRU trajectory. A csv file consists of 4 columns: ID: Measurement IDs timestamp: Timestamp in seconds x: position in x-direction in meter y: position in y-direction in meter extended_cyclist_dataset.7z : To create the Extended Cyclist Trajectory Dataset, we changed the camera lenses to capture a wider filed of view enclosing the bike lane. The dataset currently consists of 1 746 cyclist trajectories including motion primitive labels. The motion primitve labels include the classes wait, which starts at the last visbible movement of the bicycle wheel and ends the first visible bicycle wheel, starting movement, which is the frame of the first visible movement of the cyclist before the end of wait, tr/tl (turn left, turn right), which start at the first and end at the last visible turning movement of the cyclist, and hand signal left/right, which start at the first and end at the last visible frame of the hand signal. Dataset Format The dataset consists of 1746 files in json format, where every file contains one cyclist trajectory. A json file is structured as follows: {"vru_type": "bike", "trajectory2: [{"Timestamp": [LIST OF UTC TIMESTAMPS], "x": [LIST OF X POSISTIONS], "y": [LIST OF Y POSISTIONS], "z": [LIST OF Z POSISTIONS], "x_smoothed": [LIST OF SMOOTHED X POSISTIONS], "y_smoothed": [LIST OF SMOOTHED Y POSISTIONS], "z_smoothed": [LIST OF SMOOTHED Z POSISTIONS]}], "motion_primitives": {"mp_labels": [{"mp_label": LABEL NAME, "start_time": START UTC TIMESTAMP, "end_time": END UTC TIMESTAMP, ...}, ...]}} cyclist_starting_dataset.zip: The complete dataset is stored in csv format. It contains the sensor data and the corresponding label. The sensor data csv file consists of 19 columns: The data is split into meta data and payload data. The meta data contains the fields identifying an experiment, i.e., ExperimentID, SceneID, VRUID. Additionally, the Timestamp field is used to identify the corresponding sensor readings. ExperimentID: meta data, identifies each different experiment SceneID: meta data VRUID: meta data, identifier for test subject Timestamp: meta data, Timestamp, starting at 0 for each separate experiment An experiment always contains one VRU but it may consist of several scenes, i.e., starting movements. In the following the fields containing the sensor values are described. Accelerometer_x: Acceleration force along the x axis (including gravity), in m/s² Accelerometer_y: Acceleration force along the y axis (including gravity), in m/s² Accelerometer_z: Acceleration force along the z axis (including gravity), in m/s² Linear_Accelerometer_x: Acceleration force along the x axis (excluding gravity), in m/s² Linear_Accelerometer_y: Acceleration force along the y axis (excluding gravity), in m/s² Linear_Accelerometer_z: Acceleration force along the z axis (excluding gravity), in m/s² Gyroscope_x: Rate of rotation around the x axis, in rad/s Gyroscope_y: Rate of rotation around the y axis, in rad/s Gyroscope_z: Rate of rotation around the z axis, in rad/s Rotation_w: Scalar component of the rotation vector Rotation_x: Rotation vector component along the x axis, unitless Rotation_y: Rotation vector component along the y axis, unitless Rotation_z: Rotation vector component along the z axis, unitless target: indicates the label. Nothing = no label, 0 = waiting, 1 = starting_movement, 2 = starting This work results from the project DeCoInt 2, supported by the German Research Foundation (DFG) within the priority program SPP 1835: "Kooperativ interagierende Automobile", grant numbers DO 1186/1-2, FU 1005/1-2, and SI 674/11-2. Additionally, the work is supported by "Zentrum Digitalisierung Bayern". References [1] M. Goldhammer, E. Strigel, D. Meissner, U. Brunsmann, K. Doll and K. Dietmayer, "Cooperative multi sensor network for traffic safety applications at intersections," 2012 15th International IEEE Conference on Intelligent Transportation Systems, Anchorage, AK, 2012, pp. 1178-1183. doi: 10.1109/ITSC.2012.6338672 [2] M. Bieshaar, G. Reitberger, S. Zernetsch, B. Sick, E. Fuchs and K. Doll, "Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence", AAET – Automatisiertes und vernetztes Fahren, Braunschweig, pp. 67-87, Available: https://www.its-mobility.de/download/AAET/Dokumentation/AAET_2017_Tagungsband_Download.pdf [3] M. Bieshaar, S. Zernetsch, A. Hubert, B. Sick, and K. Doll. Cooperative starting movement detection of cyclists using convolutional neural networks and a boosted stacking ensemble. CoRR, abs/1803.03487, 2018.

VRUT_Dataset_complete.tar.gz：该弱势道路使用者（VRU, Vulnerable Road User）轨迹数据集包含1068条行人轨迹与464条骑行者轨迹，均通过摄像头与激光雷达（LiDAR）在城市交叉口采集完成。该交叉口的详细说明可参见文献[1]。行人轨迹通过广角立体相机系统追踪行人头部位置，并通过三角测量法生成三维位置。骑行者轨迹则通过激光雷达追踪骑行者的重心位置。摄像头采集帧率为50 Hz，激光雷达采集帧率为12.5 Hz。本数据集部分源自德国研究基金会（DFG）资助的DeCoInt²项目[2]，以及德国联邦教育与研究部（BMBF）资助的AFUSS项目。此外，本研究得到巴伐利亚数字化中心（Zentrum Digitalisierung Bayern）的支持。
数据集格式：完整数据集包含1532个CSV格式文件，每个文件对应一条弱势道路使用者轨迹。CSV文件包含4列：ID：测量编号；timestamp：时间戳，单位为秒；x：x方向位置，单位为米；y：y方向位置，单位为米。

extended_cyclist_dataset.7z：为构建扩展骑行者轨迹数据集，我们更换了相机镜头以获取覆盖自行车道的更广视场。当前数据集包含1746条骑行者轨迹，并附带运动基元标签。运动基元标签包含以下类别：等待（wait）：起始于自行车轮最后一次可见运动，终止于自行车轮首次可见运动；启动（starting movement）：对应等待阶段结束前骑行者首次可见运动的帧；左转/右转（tr/tl, turn left/turn right）：起始于骑行者首次可见转向动作，终止于最后一次可见转向动作；左右手信号（hand signal left/right）：起始于手部信号首次可见帧，终止于最后一次可见帧。
数据集格式：本数据集包含1746个JSON格式文件，每个文件对应一条骑行者轨迹。JSON文件结构如下：{"vru_type": "bike", "trajectory2": [{"Timestamp": [UTC时间戳列表], "x": [X坐标列表], "y": [Y坐标列表], "z": [Z坐标列表], "x_smoothed": [平滑X坐标列表], "y_smoothed": [平滑Y坐标列表], "z_smoothed": [平滑Z坐标列表]}], "motion_primitives": {"mp_labels": [{"mp_label": 标签名称, "start_time": 起始UTC时间戳, "end_time": 终止UTC时间戳, ...}, ...]}}

cyclist_starting_dataset.zip：完整数据集以CSV格式存储，包含传感器数据与对应标签。传感器数据CSV文件共19列，数据分为元数据与有效载荷数据两部分。元数据包含用于标识实验的字段，即ExperimentID、SceneID、VRUID。此外，Timestamp字段用于标识对应的传感器读数。
- ExperimentID：元数据，用于区分每个独立实验
- SceneID：元数据
- VRUID：元数据，受试者标识符
- Timestamp：元数据，时间戳，每个独立实验的起始时间为0
一个实验始终仅包含一名弱势道路使用者，但可包含多个场景，即多次启动动作。下文将介绍各传感器数值字段：
- Accelerometer_x：沿x轴的加速度（含重力），单位为m/s²
- Accelerometer_y：沿y轴的加速度（含重力），单位为m/s²
- Accelerometer_z：沿z轴的加速度（含重力），单位为m/s²
- Linear_Accelerometer_x：沿x轴的加速度（不含重力），单位为m/s²
- Linear_Accelerometer_y：沿y轴的加速度（不含重力），单位为m/s²
- Linear_Accelerometer_z：沿z轴的加速度（不含重力），单位为m/s²
- Gyroscope_x：绕x轴的旋转角速度，单位为rad/s
- Gyroscope_y：绕y轴的旋转角速度，单位为rad/s
- Gyroscope_z：绕z轴的旋转角速度，单位为rad/s
- Rotation_w：旋转向量的标量分量，无单位
- Rotation_x：旋转向量沿x轴的分量，无单位
- Rotation_y：旋转向量沿y轴的分量，无单位
- Rotation_z：旋转向量沿z轴的分量，无单位
- target：标签字段。无标签记为Nothing，0代表等待（waiting），1代表启动动作（starting_movement），2代表启动（starting）

本工作源自DeCoInt 2项目，得到德国研究基金会（DFG）优先项目SPP 1835：“协作交互汽车”资助，资助编号为DO 1186/1-2、FU 1005/1-2与SI 674/11-2。此外，本研究得到巴伐利亚数字化中心（Zentrum Digitalisierung Bayern）的支持。

参考文献
[1] M. Goldhammer、E. Strigel、D. Meissner、U. Brunsmann、K. Doll与K. Dietmayer，《面向交叉口交通安全应用的协作多传感器网络》，2012年第15届国际IEEE智能交通系统会议，安克雷奇，阿拉斯加，2012年，第1178-1183页，DOI: 10.1109/ITSC.2012.6338672
[2] M. Bieshaar、G. Reitberger、S. Zernetsch、B. Sick、E. Fuchs与K. Doll，《基于集体智能的弱势道路使用者意图检测》，AAET – 自动化与智能驾驶（Automatisiertes und vernetztes Fahren），不伦瑞克，第67-87页，可获取于：https://www.its-mobility.de/download/AAET/Dokumentation/AAET_2017_Tagungsband_Download.pdf
[3] M. Bieshaar、S. Zernetsch、A. Hubert、B. Sick与K. Doll，《利用卷积神经网络与增强堆叠集成实现骑行者启动动作协同检测》，CoRR，abs/1803.03487，2018年。

搜集汇总

背景与挑战

背景概述

VRU Trajectory Dataset是一个包含行人和骑自行车者轨迹的数据集，通过摄像头和LiDAR采集，提供了丰富的轨迹信息和运动原始标签。数据集分为三个部分，格式多样，适用于交通安全和自动驾驶等领域的研究。