Data underlying the MSc project: Human predictions of another vehicle at an intersection

MSc Thesis project in collaboration with TU Delft and cogniBIT GmbH on human prediction of another vehicle at an intersection. <br>30 participants were shown 168 intersection scenarios to analyse the influence of heading angle, relative position, blinkers, deceleration and lateral offset on the predicted directions. The predicted direction response is an array of [Forward%, Left%, Right%], always summing to 100%. When the participant was unsure about the direction a [33.3%, 33.3%, 33.3%] response was given (also the default option), when the participant was very confident about a direction the response would be [100%, 0%, 0%] in the left/forward/right direction.<br>The human prediction responses are stored in the .h5 files. Please read the metadata.txt file for the contents of each file.<br>To recreate the experiment, the video and image files are to be used with the experiment_gui.py from the gitlab page below. The files need to be stored in a /data/experiment/ directory or the location in the python file needs to be changed. <br>https://gitlab.tudelft.nl/tud-cor-hri/student-projects/human-predictions-in-traffic-joop<br>Within the experiment_gui.py code, the participant_ID value needs to be changed to the pseudonymized ID for the corresponding participant. The ID is also responsible for the random seed, which affects the random order the videos are shown in.<br>The experiment was approved by the TU Delft Ethics committee, and requires participant of the experiment to sign an informed consent form prior to taking part in the experiment. No PII of the participant is stored within the dataset.

本数据集为与代尔夫特理工大学（TU Delft）及cogniBIT GmbH合作开展的硕士学位论文项目，研究主题为交叉口场景下人类对其他车辆的行驶意图预测。
本次实验共招募30名参与者，向其展示168种交叉口场景，以分析航向角、相对位置、转向灯状态、减速行为以及横向偏移量对车辆预测行驶方向的影响。参与者的预测响应为包含[前进占比、左转占比、右转占比]的数组，三者之和恒为100%。当参与者对车辆行驶方向不确定时，将给出[33.3%, 33.3%, 33.3%]作为默认响应；若参与者对某一行驶方向有十足把握，则会输出对应方向占比为100%、其余方向占比为0%的结果，例如[100%, 0%, 0%]。
人类预测的响应数据存储于.h5格式文件中，各文件的具体内容请参阅metadata.txt文件。
若需复现本次实验，需使用下方GitLab页面提供的experiment_gui.py脚本，并搭配对应的视频与图像文件。实验文件需存放于/data/experiment/目录下，或修改Python脚本中的文件路径配置。
https://gitlab.tudelft.nl/tud-cor-hri/student-projects/human-predictions-in-traffic-joop
在experiment_gui.py代码中，需将participant_ID参数修改为对应参与者的匿名化ID，该ID同时用于设置随机种子，以控制实验视频的随机播放顺序。
本实验已通过代尔夫特理工大学伦理委员会审批，参与者需在实验开始前签署知情同意书。数据集未存储任何参与者的个人可识别信息（Personally Identifiable Information，PII）。