Abstract
Driver fatigue leading to drowsy driving is a severe traffic safety problem and is widely believed to be one of the largest contributors to fatalities and severe injuries on the road at present. Nodding off for just three seconds or less while driving can prove fatal. Drowsy driving slows reaction times, reduces vigilance, impairs information processing and creates un-mindfulness. We have developed a detection system for drivers under drowsiness, using non-invasive sensors. The system uses brain–computer interface (BCI) to determine the mental attention level of the driver following a complex recursive algorithm. In order to reduce false alarms in such detection system, we have incorporated two additional sensors in it. Infrared (IR) trans-receiver system emits an infrared signal to the eyes and another infrared photoresistor measures the reflected wave. The reflectivity of the open eye is grossly different from closed eye owing to the structure and presence of tear film in the eye. The microcontroller continuously compares and detects the difference in eye-blinking patterns of a normal person and that of a driver under drowsiness. The sleeping driver has certainly less or no eye blinking, which will be detected online and immediately without any time lag to prevent accident. Finally, a 3-axis compass sensor placed on the steering wheel will detect further the angular movement of the steering wheel of the vehicle. The driver under drowsiness will show an irregularity in eye-blinking pattern together with an abnormality in steering movement. On coincidence of all the three sensors, in order to reduce any false alarm, the driver will be alerted with a blinking LED placed within his/her view angle. If the driver does not respond and the statistics do not come back to normal, the software would prompt to apply emergency brakes automatically and simultaneously it would send SMS/email to the concerned authorities. The vehicle may also be fitted with additional blinking lights visible to other drivers too, to alert them on the road.
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
National Highway Traffic Safety Administration. Research on Drowsy Driving. Accessed October 20, 2015.
Masten SV, Stutts JC, Martell CA. Predicting daytime and nighttime drowsy driving crashes based on crash characteristic models. 50th Annual Proceedings, Association for the Advancement of Automotive Medicine; October 2006; Chicago, IL.
Klauer SG, Dingus TA, Neale VL, Sudweeks JD, Ramsey DJ. The Impact of Driver Inattention on Near-Crash/Crash Risk: An Analysis Using the 100-Car Naturalistic Study Data, 2006. Springfield, VA: DOT; year. DOT HS 810 594.
Tefft BC, AAA Foundation for Traffic Safety. Prevalence of Motor Vehicle Crashes Involving Drowsy Drivers, United States, 2009–2013 [457 KB]. Washington, DC: AAA Foundation for Traffic Safety; 2014. October 19, 2015.
Tengshe, V. V. and V. G. Tengshe (2007). Drowsy driving alarm system, Google Patents.
Parmar, N. (2002). “Drowsy driver detection system.” Engineering Design Project Thesis, Ryerson University.
Wu, R., et al. (2016). Drowsy driver detection system, Google Patents.
Acknowledgments
We wish to thank Dr. Sanat Kr. Das and Dr. Abhijit Chatterjee for their constant valuable inputs throughout the work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sinha, O., Singh, S., Mitra, A., Ghosh, S.K., Raha, S. (2018). Development of a Drowsy Driver Detection System Based on EEG and IR-based Eye Blink Detection Analysis. In: Bera, R., Sarkar, S., Chakraborty, S. (eds) Advances in Communication, Devices and Networking. Lecture Notes in Electrical Engineering, vol 462. Springer, Singapore. https://doi.org/10.1007/978-981-10-7901-6_34
Download citation
DOI: https://doi.org/10.1007/978-981-10-7901-6_34
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7900-9
Online ISBN: 978-981-10-7901-6
eBook Packages: EngineeringEngineering (R0)