Abstract
Hazardous driver states are the cause of many traffic accidents, and there is therefore a great need for accurate detection of such states. This study proposes a new classification method that is evaluated on a previously collected driving dataset that includes combinations of four causes of hazardous driver states: drowsiness, high traffic density, adverse weather, and cell phone usage. The previous study consisted of four sessions and eight scenarios within each session. Four physiological signals (e.g. electrocardiogram) and eight vehicle kinematics signals (e.g. throttle, road offset) were recorded during each scenario. In both previous and present studies, the presence or absence of the different causes of hazardous driver states was classified. In this study, a new classifier based on principal component analysis and artificial neural networks is proposed. The obtained results show improvement across all classification accuracies, especially when only vehicle kinematics data are used (mean of 12.7%).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
National Highway Traffic Safety Administration (NHTSA): 2015 Motor Vehicle Crashes: Overview (2016)
Zhang, L., et al.: Cognitive load measurement in a virtual reality-based driving system for autism intervention. IEEE Trans. Affect. Comput. 8, 176–189 (2017)
Healey, J.A., Picard, R.W.: Detecting stress during real-world driving tasks using physiological sensors. IEEE Trans. Intell. Transp. Syst. 6, 156–166 (2005)
Recarte, M.A., Nunes, L.M.: Mental workload while driving: effects on visual search, discrimination, and decision making. J. Exp. Psychol. Appl. 9, 119–137 (2003)
Hwang, Y., Yoon, D., Kim, H.S., Kim, K.H.: A validation study on a subjective driving workload prediction tool. IEEE Trans. Intell. Transp. Syst. 15, 1835–1843 (2014)
Lu, Z., Coster, X., de Winter, J.: How much time do drivers need to obtain situation awareness? A laboratory-based study of automated driving. Appl. Ergon. 60, 293–304 (2017)
Chuang, C.-H., Cao, Z., King, J.-T., Wu, B.-S., Wang, Y.-K., Lin, C.-T.: Brain electrodynamic and hemodynamic signatures against fatigue during driving. Front. Neurosci. 12 (2018)
Guo, M., Li, S., Wang, L., Chai, M., Chen, F., Wei, Y.: Research on the relationship between reaction ability and mental state for online assessment of driving fatigue. Int. J. Environ. Res. Public Health. 13, 1174 (2016)
Jung, S.-J., Shin, H.-S., Chung, W.-Y.: Driver fatigue and drowsiness monitoring system with embedded electrocardiogram sensor on steering wheel. IET Intell. Transp. Syst. 8, 43–50 (2014)
Collet, C., Clarion, A., Morel, M., Chapon, A., Petit, C.: Physiological and behavioural changes associated to the management of secondary tasks while driving. Appl. Ergon. 40, 1041–1046 (2009)
Darzi, A., Gaweesh, S.M., Ahmed, M.M., Novak, D.: Identifying the causes of drivers’ hazardous states using driver characteristics, vehicle kinematics, and physiological measurements. Front. Neurosci. 12 (2018)
Darzi, A., Gorsic, M., Novak, D.: Difficulty adaptation in a competitive arm rehabilitation game using real-time control of arm electromyogram and respiration. In: 2017 International Conference on Rehabilitation Robotics (ICORR), pp. 857–862. IEEE (2017)
Kajiwara, S.: Evaluation of driver’s mental workload by facial temperature and electrodermal activity under simulated driving conditions. Int. J. Automot. Technol. 15, 65–70 (2014)
Jun, J., Guensler, R., Ogle, J.: Differences in observed speed patterns between crash-involved and crash-not-involved drivers: application of in-vehicle monitoring technology. Transp. Res. Part C Emerg. Technol. 19, 569–578 (2011)
Zheng, Y., Hansen, J.H.L.: Lane-change detection from steering signal using spectral segmentation and learning-based classification. IEEE Trans. Intell. Veh. 8858, 1 (2017)
Sun, R., Ochieng, W.Y., Feng, S.: An integrated solution for lane level irregular driving detection on highways. Transp. Res. Part C Emerg. Technol. 56, 61–79 (2015)
Kordani, A.A., Molan, A.M., Monajjem, S.: New formulas of side friction factor based on three-dimensional model in horizontal curves for various vehicles. In: T&DI Congress 2014, pp. 592–601. American Society of Civil Engineers, Reston (2014)
Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3, 71–86 (1991)
Acknowledgments
Research supported by the National Science Foundation under grant no. 1717705 as well as by the National Institute of General Medical Sciences of the National Institutes of Health under grant no. P20GM103432.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Aghajarian, M., Darzi, A., McInroy, J.E., Novak, D. (2019). A New Method for Classification of Hazardous Driver States Based on Vehicle Kinematics and Physiological Signals. In: Karwowski, W., Ahram, T. (eds) Intelligent Human Systems Integration 2019. IHSI 2019. Advances in Intelligent Systems and Computing, vol 903. Springer, Cham. https://doi.org/10.1007/978-3-030-11051-2_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-11051-2_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-11050-5
Online ISBN: 978-3-030-11051-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)