Data underlying the publication: Modelling perceived risk and trust in driving automation reacting to merging and braking vehicles

doi: 10.4121/95a4bb4e-3ca4-4fcc-ba34-4be76a9ab578.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/95a4bb4e-3ca4-4fcc-ba34-4be76a9ab578
Datacite citation style:
He, Xiaolin; Stapel, J.C.J. (Jork); Wang, Meng; R. (Riender) Happee (2023): Data underlying the publication: Modelling perceived risk and trust in driving automation reacting to merging and braking vehicles. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/95a4bb4e-3ca4-4fcc-ba34-4be76a9ab578.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset

This dataset is derived from a driving simulator study that explored the dynamics of perceived risk and trust in the context of driving automation. The study involved 25 participants who were tasked with monitoring SAE Level 2 driving automation features (Adaptive Cruise Control and Lane Centering) while encountering various driving scenarios on a motorway. These scenarios included merging and hard-braking events with different levels of criticality. 

This dataset contains kinetic data from the driving simulator, capturing variables such as vehicle position, velocity, and acceleration among others. Subjective ratings of perceived risk and trust, collected post-event for regression analysis are also included.

history
  • 2023-10-20 first online, published, posted
publisher
4TU.ResearchData
format
*.mat, *.xlsx
funding
  • Horizon 2020 - SHAPE-IT (grant code 860410) European Union’s Horizon 2020
organizations
Delft University of Technology, Faculty of Mechanical, Maritime and Materials Engineering, Department of Cognitive Robotics
Technische Universität Dresden, “Friedrich List” Faculty of Transport and Traffic Sciences, Chair of Traffic Process Automation

DATA

files (2)