This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.
Vaishnavi Kaushal,
Riya Sharma,
Shivani Choudhary,
- Student, Department of Electronics and Communication Engineering, IMS Engineering College, Dasna, Ghaziabad, Uttar Pradesh, India
- Student, Department of Electronics and Communication Engineering, IMS Engineering College, Dasna, Ghaziabad, Uttar Pradesh, India
- Student, Department of Electronics and Communication Engineering, IMS Engineering College, Dasna, Ghaziabad, Uttar Pradesh, India
Abstract
Driver fatigue, alcohol and slow response of emergency services are among a significant issue of road accidents. The paper will provide a real-life example of an IoT-based vehicle safety system, which will combine the accident detection, driver drowsiness and alcohol sensors with a cohesive system. The proposed system consists of use of accelerometer for sudden collision detection, infrared eye blink sensor for determining alertness of the driver and MQ-3 alcohol sensor for determining the concentration of pneumonia in the breath of the driver. The sensor data is processed using a microcontroller which under the abnormal circumstances, sends real time alerts and GPS location information using a GSM communication module. The system incorporates more than one sensing scheme and, therefore, minimize false alarms and increase reliability, as opposed to single- sensor designs. The suggested model will be used to provide scalable and cost-efficient solution to road safety improvement. The integrated system facilitates prompt connection with emergency contacts and allows for the quick assessment of dangerous driving circumstances. The suggested architecture can help lessen the severity of accidents and enhance emergency response by continuously monitoring important driver and vehicle information. The system’s real-time sensing, position tracking, and Internet of Things connectivity make it feasible to implement in contemporary cars. Overall, the system’s minimal implementation complexity and cost allow it to be modified for various vehicle kinds and operating circumstances.
Keywords: The IoT; vehicle accident detection; driver drowsiness monitoring; alcohol sensing system; embedded safety systems.
References
- World Health Organization. Global status report on road safety 2023. World Health Organization; 2023 Dec 12.
- National Highway Traffic Safety Administration. Traffic safety facts annual report tables. National Highway Traffic Safety Administration. 2018.
- Sahayadhas A, Sundaraj K, Murugappan M. Detecting driver drowsiness based on sensors: a review. Sensors. 2012 Dec 7;12(12):16937-53.
- Singh S. Critical reasons for crashes investigated in the national motor vehicle crash causation survey. 2015 Feb..
- Megalingam RK, Nair RN, Prakhya SM. Wireless vehicular accident detection and reporting system. In2010 International Conference on Mechanical and Electrical Technology 2010 Sep 10 (pp. 636-640). IEEE.
- Mukhopadhyay S, Kumar A, Gupta J, Bhatnagar A, Kantipudi MP. International Journal of Transport Development and Integration. Integration. 2024 Mar;8(1):61-77.
- Ji Q, Zhu Z, Lan P. Real-time nonintrusive monitoring and prediction of driver fatigue. IEEE transactions on vehicular technology. 2004 Jul 31;53(4):1052-68.
- Danisman T, Bilasco IM, Djeraba C, Ihaddadene N. Drowsy driver detection system using eye blink patterns. In2010 International Conference on Machine and Web Intelligence 2010 Oct 3 (pp. 230-233). IEEE.
- Du S, Ibrahim M, Shehata M, Badawy W. Automatic license plate recognition (ALPR): A state-of-the-art review. IEEE Transactions on circuits and systems for video technology. 2012 Jun 7;23(2):311-25.
- Pandey AD, Kumar B, Parida M, Mudgal A, Chouksey AK, Mishra R. Vehicle classification using accelerometer signals and machine-learning techniques. Journal of Intelligent Transportation Systems. 2025 Apr 26:1-29.
- Ibrahim HA, Aly AK, Far BH. A system for vehicle collision and rollover detection. In2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) 2016 May 15 (pp. 1-6). IEEE.
- Khan MQ, Lee S. Gaze and eye tracking: Techniques and applications in ADAS. Sensors. 2019 Dec 14;19(24):5540.
- Wen Z, Chen J, Yeh MH, Guo H, Li Z, Fan X, Zhang T, Zhu L, Wang ZL. Blow-driven triboelectric nanogenerator as an active alcohol breath analyzer. Nano Energy. 2015 Sep 1;16:38-46.
- Sapthami I, Raju VN, Vaithianathan V, Chinnasamy P, Kumaran G, Ayyasamy RK. IoT based alcohol detection and vehicle control system. In2024 5th international conference on data intelligence and cognitive informatics (ICDICI) 2024 Nov 18 (pp. 262-266). IEEE.
- Atzori L, Iera A, Morabito G. The internet of things: A survey. Computer networks. 2010 Oct 28;54(15):2787- 805..
- Evans D. The internet of things. How the Next Evolution of the Internet is Changing Everything, Whitepaper, Cisco Internet Business Solutions Group (IBSG). 2011 Apr 30;1:1-2.
- Ortiz FM, Sammarco M, Costa LH, Detyniecki M. Vehicle telematics via exteroceptive sensors: A survey. arXiv preprint arXiv:2008.12632. 2020 Aug 27.
- Gupta A, Singh S, Asari V, Nguyen TV. Enhancing Sustainability and Construction Safety Research in the Era of Artificial Intelligence. ASME Journal of Engineering for Sustainable Buildings and Cities. 2026 May 1;7(2):020802.
- Sahraei MA, Al Mamari SR. A review of Internet of Things approaches for vehicle accident detection and emergency notification. Sustainability. 2025 Jul 16;17(14):6510.
- Chen M, Mao S, Liu Y. Big data: A survey. Mobile networks and applications. 2014 Apr;19(2):171-209.
International Journal of Electronics Automation
| Volume | 04 | |
| 02 | ||
| Received | 24/07/2026 | |
| Accepted | 05/08/2026 | |
| Published | 10/08/2026 | |
| Publication Time | 17 Days |