Abstract
Driver drowsiness is a major cause of automobile accidents, resulting in many fatalities each year. The use of face detection techniques for identifying and warning fatigued drivers can solve this problem and improve transportation safety. This technique detects drowsiness using computer vision technologies based on facial landmarks. Image processing is used by the system to recognize the driver’s face, extract pictures of the eyes, and detect tiredness. The camera monitors the driver’s eyes in real-time, processing visual data to detect symptoms of tiredness. When sleepiness is detected, an alarm sounds to wake and notify the driver. Furthermore, the system is deployed with an Arduino UNO board with GSM, GPS, and MPU 6050 models to detect and alert emergency services in the case of a car accident, as well as to notify the driver’s family. Here the MPU- 6050 sensor detects angular position changes, while the NEO- 6m GPS module identifies the vehicle’s location. The SIM800L GSM module delivers an SMS including the location as well as a warning message. This system could be kept in automobiles to improve driver and passenger safety and security.
References
- “Road accident fatalities continue unabated,” The Himalayan Times, Nov. 2017.
- H. Su and G. Zheng, “A partial least squares regression-based fusion model for predicting the trend in drowsiness,” IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART A: SYSTEMS AND HUMANS., vol. 38, no. 5, Sep. 2008.
- F. Friedrichs and B. Yang, “Camera-based drowsiness reference for driver state classification under real driving conditions,” 2010 IEEE Intelligent Vehicles Symposium University of California, San Diego, CA, USA, pp. 21–24, Jun. 2010.
- B. L. Y. Zhang, Wei; Cheng, “Driver drowsiness recognition based on computer vision technology,” Published in: Tsinghua Science and Technology, vol. 17, pp. 354 – 362, Jun. 2012.
- M. L. Eyosiyas Tadesse, Weihua Sheng, “Driver drowsiness detection through hmm-based dynamic modeling.” 2014 IEEE International Con- ference on Robotics Automation (ICRA) Hong Kong Convention and Exhibition Center, May 31- June 7 2014.
- R. V. S. Reddy and P. A. Kumari, “Internet of things (iot) based multi- level drunken driving detection and prevention system using raspberry pi 3,” International Journal of Computer Science and Information Security (IJCSIS), p. 131 – 137, 2020.
- V. Kinage and P. Patil, “Iot based intelligent system for vehicle accident prevention and detection at real time,” in 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), p. 409 – 413, 2019.
- R. L. e. a. Y. Ma, B. Chen, “Driving fatigue detection from eeg using a modified pcanet method,” Computational Intelligence and Neuro- science, vol. 2019, p. 1–9, 2019.
- “Losing paradise,” YouTube, 29 October 2012, 8 December.
- M. B. I. R. Md. Syedul Amin, Jubayer Jalil, “Accidentdetection and reporting system using gps, gprs and gsm technology,” 2012 Published in Informatics, Electronics Vision (ICIEV), 2012.
- “Manage crash detection on iphone 14 models,” Apple Support.[Online]. Available: support.apple.com/en-gb/guide/iphone/iph948a628e9/ios.
- S. B. Prateek Verma, Maheedhar Dubey and P. Verma, “Hough transform method for iris recognition-a biometric approach,” International Journal Of Engineering and Innovative Technology (IJEIT), vol. 1, Jun. 2012