Driving simulator dataset on human driven vehicles' gap acceptance behaviour in mixed traffic with automated vehicles

This research is based on data gathered in 2020 at Delft University of Technology. This dataset is from a driving simulator experiment, whose goal was to study human drivers' behaviour in mixed traffic, which has both human-driven vehicles and automated vehicles (AVs).<br>The dataset consists of 95 participants of which 71 (74.7 %) were male and 24 females.<br>The route in the driving simulator consisted of several motorway sections, provincial (regional) road sections, and three priority T-intersections. Each T-intersection consisted of an urban road (the minor road) intersecting with a provincial road (the major road). The defined speed limit was 100 km/h on the motorway, 80 km/h on the provincial roads, and 50 km/h on urban roads. On the motorway, drivers also experienced dynamic speed limit sections. A depiction of the route is attached in this dataset information.<br>The experiment design aimed to separately observe the effects of AVs’ recognizability and their driving style on human driving behavior as well as their combined effects. Each participant drove four scenarios, excluding a familiarization drive. The scenarios differed in two aspects: the recognizability and the driving style of AVs.Two variables primarily varied in the experiment: the driving style of AVs, and their recognizability. The participants were assigned randomly to one of three groups: Defensive AVs, Aggressive AVs, and Mixed AVs. The group determined the driving style of AVs that a participant encountered in the experiment.<br>More details about the experiment set-up itself can be found in our paper published: https://doi.org/10.1016/j.trf.2022.09.018<br><strong>Attached files:</strong>Processed datasetReadMe fileData processing scripts<br><strong>File formats:</strong>Data /.csvData /.xlsxJupyternotebooks /.ipynb