Supplementary data for the paper 'What makes a good driver on public roads and race tracks? An interview study'

doi: 10.4121/14483220.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: 10.4121/14483220
Datacite citation style:
Doubek, F. (Fabian); Salzmann, Falk; de Winter, Joost (2021): Supplementary data for the paper 'What makes a good driver on public roads and race tracks? An interview study'. Version 1. 4TU.ResearchData. dataset.
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
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Future vehicles may drive automatically in a human-like manner or contain systems that monitor human driving ability. Algorithms of these systems must have knowledge of criteria of good and safe driving behavior with regard to different driving styles. In the current study, interviews were conducted with 30 drivers, including driving instructors, engineering, and race drivers. The participants were asked to describe good driving on public roads and race tracks, and in some questions were supported with video material. The results were interpreted with the help of Endsley’s model of situation awareness. The interviews showed that there were clear differences between what was considered good driving on the race track and good driving on the public road, where for the former, the driver must touch the limit of the vehicle, whereas, for the latter, the limit should be avoided. However, in both cases, a good driver was characterized by self-confidence, lack of stress, and not being aggressive. Furthermore, it was mentioned that the driver’s posture and viewing behavior are essential components of good driving, which affect the driver’s prediction of events and execution of maneuvers. The implications of our findings for the development of automation technology are discussed. In particular, we see potential in driver posture estimation and argue that automated vehicles excel in perception but may have difficulty making predictions.

  • 2021-05-19 first online, published, posted
Delft University of Technology, Faculty of Mechanical, Maritime and Materials Engineering

Department of Connectivity, Dr. Ing. h.c. F. Porsche AG, Germany


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