Abstract
Previous studies suggest that variation in driver’s states, such as being under stress, can degrade drivers’ performance. Moreover, different drivers may have varying behaviors and reactions in different road conditions and environments (contexts). Thus, personalized driver models given different contextual settings can assist in better predicting the drivers’ states (behavioral and psychological); this can then allow vehicles to adjust the driving experience around the driver and passengers’ preferences and comfort levels. This paper aims at developing personalized hierarchical driver’s state models by considering driver’s heart rate variability (HRV) in relation to the changes in various contextual settings of road, weather, and presence of a passenger. Results from 12 participants over 150 h of driving data suggest that drivers are on average less stressed in highways compared to cities, when being with a passenger compared to alone, and when driving in non-rainy conditions compared to rainy weather.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Hu, T.Y., Xie, X., Li, J.: Negative or positive? The effect of emotion and mood on risky driving. Transp. Res. Part F Traffic Psychol. Behav. 16, 29–40 (2013)
Jeon, M., Walker, B.N., Yim, J.B.: Effects of specific emotions on subjective judgment, driving performance, and perceived workload. Transp. Res. Part F Traffic Psychol. Behav. 24, 197–209 (2014)
Milleville-Pennel, I., Charron, C.: Driving for real or on a fixed-base simulator: is it so different? An explorative study. Presence Teleoperators Virtual Environ. 24(1), 74–91 (2015)
Healey, J., Picard, R.W., et al.: Detecting stress during real-world driving tasks using physiological sensors. IEEE Trans. Intell. Transp. Syst. 6(2), 156–166 (2005)
Eyal, S., Akselrod, S.: Heart rate variability (HRV): nonlinear HRV. In: Wiley Encyclopedia of Biomedical Engineering (2006)
Scholkmann, F., Boss, J., Wolf, M.: An efficient algorithm for automatic peak detection in noisy periodic and quasi-periodic signals. Algorithms 5(4), 588–603 (2012). https://doi.org/10.3390/a5040588. http://www.mdpi.com/1999-4893/5/4/588
Hill, J.D., Boyle, L.N.: Driver stress as influenced by driving maneuvers and road- way conditions. Transp. Res. Part F Traffic Psychol. Behav. 10(3), 177–186 (2007)
Punita, P., Saranya, K., Kumar, S.: Gender difference in heart rate variability in medical students and association with the level of stress. Natl. J. Physiol. Pharm. Pharmacol. 6(5), 431–437 (2016)
Kilpeläinen, M., Summala, H.: Effects of weather and weather forecasts on driver behaviour. Transp. Res. Part F Traffic Psychol. Behav. 10(4), 288–299 (2007)
Wilde, G.J.: Roadside aesthetic appeal, driver behaviour and safety. Can. J. Transp. 3(1) (2009)
Shinar, D., Compton, R.: Aggressive driving: an observational study of driver, vehicle, and situational variables. Accid. Anal. Prev. 36(3), 429–437 (2004)
Boukhechba, M., Barnes, L.E.: SWear: Sensing using WEARables. Generalized Human Crowdsensing on Smartwatches. In: 2019 IEEE 11th International Conference on Applied Human Factors and Ergonomics. IEEE (2020)
Osborne, J.W.: Advantages of hierarchical linear modeling. Pract. Assess. Res. Eval. 7(1), 1 (2000)
Antonson, H., Mårdh, S., Wiklund, M., Blomqvist, G.: Effect of surrounding land-scape on driving behaviour: a driving simulator study. J. Environ. Psychol. 29(4), 493–502 (2009)
Armony, J., Vuilleumier, P.: The Cambridge Handbook of Human Affective Neuroscience. Cambridge University Press, Cambridge (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Tavakoli, A., Boukhechba, M., Heydarian, A. (2020). Personalized Driver State Profiles: A Naturalistic Data-Driven Study. In: Stanton, N. (eds) Advances in Human Aspects of Transportation. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1212. Springer, Cham. https://doi.org/10.1007/978-3-030-50943-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-50943-9_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-50942-2
Online ISBN: 978-3-030-50943-9
eBook Packages: EngineeringEngineering (R0)