A multimodal dataset for various forms of distracted driving
Abstract
We describe a multimodal dataset acquired in a controlled experiment on a driving simulator. The set includes data for n=68 volunteers that drove the same highway under four different conditions: No distraction, cognitive distraction, emotional distraction, and sensorimotor distraction. The experiment closed with a special driving session, where all subjects experienced a startle stimulus in the form of unintended acceleration—half of them under a mixed distraction, and the other half in the absence of a distraction. During the experimental drives key response variables and several explanatory variables were continuously recorded. The response variables included speed, acceleration, brake force, steering, and lane position signals, while the explanatory variables included perinasal electrodermal activity (EDA), palm EDA, heart rate, breathing rate, and facial expression signals; biographical and psychometric covariates as well as eye tracking data were also obtained. This dataset enables research into driving behaviors under neatly abstracted distracting stressors, which account for many car crashes. The set can also be used in physiological channel benchmarking and multispectral face recognition.
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Overall, problems were of small to moderate scale and consistent with wearability limitations for BioHarness and E4.
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From Stress and productivity patterns of interrupted, synergistic, and antagonistic office activities (2019) · cites this paper as [27]
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Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research
(1988) · Advances in Psychology · doi:10.1016/s0166-4115(08)62386-9 · Cited by 15,003 · see citing works
Spielberger, C. D. (2010) · The Corsini Encyclopedia of Psychology · doi:10.1002/9780470479216.corpsy0943 · Cited by 2,535 · see citing works
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Research works printed but not linked: 15
- Open Science Framework Taamneh S. 2016 https://doi.org/10.17605/OSF.IO/C42CN · DOI
- McEvoy S. P., Stevenson M. R. & Woodward. M. The prevalence of, and factors associated with, serious crashes involving a distracting activity. Accident Analysis & Prevention 39, 475– 482 (2007). 17034748
- Cooper J. M., Vladisavljevic I., Medeiros-Ward N., Martin P. T. & Strayer. D. L. An investigation of driver distraction near the tipping point of traffic flow stability. Human Factors: The Journal of the Human Factors and Ergonomics Society 51, 261– 268 (2009). 19653488
- Donmez B., Boyle L. N. & Lee J. D. Mitigating driver distraction with retrospective and concurrent feedback. Accident Analysis & Prevention 40, 776– 786 (2008). 18329433
- Pavlidis I. et al. Dissecting driver behaviors under cognitive, emotional, sensorimotor, and mixed stressors. Scientific Reports 6, 1– 12 (2016). 27170291 PMC4867684
- Shastri D., Papadakis M., Tsiamyrtzis P., Bass B. & Pavlidis I. Perinasal imaging of physiological stress and its affective potential. IEEE Transactions on Affective Computing 3, 366– 378 (2012).
- Poh M. Z., McDuff D. J. & Picard R. W. Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Optics Express 18, 10762– 10774 (2010). 20588929
- Garbey M., Sun N., Merla A. & Pavlidis I. Contact-free measurement of cardiac pulse based on the analysis of thermal imagery. IEEE Transactions on Biomedical Engineering 54, 1418– 1426 (2007). 17694862
- Wesley A., Buddharaju P., Pienta R. & Pavlidis I. A comparative analysis of thermal and visual modalities for automated facial expression recognition. International Symposium on Visual Computing 51– 60 (2012).
- Perry A. R. & Baldwin D. A. Further evidence of associations of type A personality scores and driving-related attitudes and behaviors. Perceptual and Motor Skills 91, 147– 154 (2000). 11011886
- Hart S. G. & Staveland L. E. in Human Mental Workload (eds Hancock P. A. & Meshkati N.) 139– 183 (North Holland Press, 1988).
- Zhou Y., Tsiamyrtzis P., Lindner P., Timofeyev I. & Pavlidis I. Spatio-temporal smoothing as a basis for facial tissue tracking in thermal imaging. IEEE Trans. Biomed. Eng. 60, 1280– 1289 (2013). 23247840
- Gwen L. et al. The computer expression recognition toolbox (CERT). 2011 IEEE International Conference on Automatic Face & Gesture Recognition and Workshops (FG 2011) 298– 305 (2011).
- Tsiamyrtzis P., Dcosta M., Shastri D., Prasad E. & Pavlidis I. Delineating the operational envelope of mobile and conventional EDA sensing on key body locations. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems 5665– 5674 (2016).
- Tsiamyrtzis P. et al. Imaging facial physiology for the detection of deceit. International Journal of Computer Vision 71, 197– 214 (2007).
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A multimodal driver monitoring benchmark dataset for driver modeling in assisted driving automation
Dargahi Nobari, K., & Bertram, T. (2024) · Scientific Data · doi:10.1038/s41597-024-03137-y · Cited by 29 · see citing works
Stress and productivity patterns of interrupted, synergistic, and antagonistic office activities
Zaman, S., Wesley, A., Silva, D. R. D. C., Buddharaju, P., Akbar, F., Gao, G., Mark, G., Gutierrez-Osuna, R., & Pavlidis, I. (2019) · Scientific Data · doi:10.1038/s41597-019-0249-5 · Cited by 17 · see citing works
Self-citation
- Authors
- Taamneh, S., Tsiamyrtzis, P., Dcosta, M., Buddharaju, P., Khatri, A., Manser, M., Ferris, T., Wunderlich, R., & Pavlidis, I.
- Venue
- Scientific Data
- Published
- 2017-08-15
- Volume
- 4
- Issue
- 1
- ISSN
- 2052-4463
- PMC
- PMC5827115
- Cited by
- 105
- References
- 2
- Language
- en
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