Abstract
According to the statistics from World Health Organization (WHO), traffic accidents have become one of the top ten leading causes of death in the world. Specifically, traffic accidents claimed nearly 3500 lives each day in 2014. Studies show that most traffic accidents are caused by human factors, e.g. drivers’ abnormal driving behaviors. Therefore, it is necessary to detect drivers’ abnormal driving behaviors to alert the drivers or report Transportation Bureau to record them.
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
Y. Wang, J. Yang, H. Liu, Y. Chen, M. Gruteser, and R. P. Martin, “Sensing vehicle dynamics for determining driver phone use,” in Proceeding of the 11th annual international conference on Mobile systems, applications, and services (ACM Mobisys 2013), pp. 41–54, 2013.
H. Han, J. Yu, H. Zhu, Y. Chen, J. Yang, Y. Zhu, G. Xue, and M. Li, “Senspeed: Sensing driving conditions to estimate vehicle speed in urban environments,” in Proceedings of IEEE Conference on Computer Communications (IEEE INFOCOM 2014), pp. 727–735, 2014.
World Health Organisation, “The top ten causes of death.” [Online], Available: http://www.who.int/mediacentre/factsheets/fs310/en/, 2017.
C. Saiprasert and W. Pattara-Atikom, “Smartphone enabled dangerous driving report system,” in Proceedings of 46th Hawaii International Conference on System Sciences (IEEE HICSS 2013), pp. 1231–1237, 2013.
M. V. Yeo, X. Li, et al., “Can SVM be used for automatic EEG detection of drowsiness during car driving?,” Safety Science, vol. 47, no. 1, pp. 115–124, 2009.
S. Al-Sultan, A. H. Al-Bayatti, and H. Zedan, “Context-aware driver behavior detection system in intelligent transportation systems,” IEEE transactions on vehicular technology (IEEE TVT), vol. 62, no. 9, pp. 4264–4275, 2013.
S. Reddy, M. Mun, J. Burke, D. Estrin, M. Hansen, and M. Srivastava, “Using mobile phones to determine transportation modes,” ACM Transactions on Sensor Networks (ACM TOSN), vol. 6, no. 2, pp. 13–40, 2010.
J. Dai, J. Teng, et al., “Mobile phone based drunk driving detection,” in Proceedings of EAI International Conference on Pervasive Computing Technologies for Healthcare (EAI PervasiveHealth 2010), pp. 1–8, 2010.
M. Fazeen, B. Gozick, R. Dantu, M. Bhukhiya, and M. C. González, “Safe driving using mobile phones,” IEEE Transactions on Intelligent Transportation Systems (IEEE TITS), vol. 13, no. 3, pp. 1462–1468, 2012.
U.S.NHTSA, “The visual detection of DWI motorists.” [Online], Available: http://www.shippd.org/Alcohol/dwibooklet.pdf, 2015.
Z. Chen, J. Yu, Y. Zhu, Y. Chen, and M. Li, “D3: Abnormal driving behaviors detection and identification using smartphone sensors,” in Proceedings of 12th Annual IEEE International Conference on Sensing, Communication, and Networking (IEEE SECON 2015), pp. 524–532, 2015.
J. Yu, Z. Chen, Y. Zhu, Y. J. Chen, L. Kong, and M. Li, “Fine-grained abnormal driving behaviors detection and identification with smartphones,” IEEE Transactions on Mobile Computing (IEEE TMC), vol. 16, no. 8, pp. 2198–2212, 2017.
P. Harrington, “Machine learning in action”, vol. 5. Manning Greenwich, CT, 2012.
Y. Guo, L. Yang, X. Ding, J. Han, and Y. Liu, “Opensesame: Unlocking smart phone through handshaking biometrics,” in Proceedings of IEEE Conference on Computer Communication (IEEE INFOCOM 2013), pp. 365–369, 2013.
C. Chang and C. Lin, “Libsvm: A library for support vector machines,” ACM Transactions on Intelligent Systems and Technology (ACM TIST), vol. 2, no. 3, pp. 27–65, 2011.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2018 The Author(s), under exclusive licence to Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Yu, J., Chen, Y., Xu, X. (2018). Sensing Vehicle Dynamics for Abnormal Driving Detection. In: Sensing Vehicle Conditions for Detecting Driving Behaviors. SpringerBriefs in Electrical and Computer Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-89770-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-89770-7_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-89769-1
Online ISBN: 978-3-319-89770-7
eBook Packages: Computer ScienceComputer Science (R0)