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@article{174992,
        author = {Khush Kuhad and Bhavika Tanwar and Manish Kumar Jain K and Yashika and Mehek Jain},
        title = {Intelligent Ride Acceptance System Using Alcohol and Fatigue Detection for Drivers},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {8106-8117},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174992},
        abstract = {In this paper, we propose an intelligent 
ride acceptance system that enhances driver and 
passenger safety by integrating alcohol and 
fatigue detection mechanisms into the ride- 
hailing process. Before a ride is accepted, the 
system performs a breath alcohol test using an 
MQ-3 gas sensor interfaced with a Raspberry 
Pi 
and simultaneously evaluates driver 
alertness via a camera module and real-time 
computer vision analysis for drowsiness. A 
Flask-based API transmits the sensor data and 
detection results to a cab company’s backend, 
allowing verification of the driver’s sobriety and 
alertness in real-time. We present the design and 
implementation of the system, including 
hardware integration, software algorithms for 
alcohol sensing and eye-closure detection, and the 
communication protocol for data exchange. 
Experimental simulations demonstrate that the 
system can reliably block ride requests if 
alcohol is detected and can alert drowsy drivers 
with a buzzer, logging such events for review. 
This approach has the potential to reduce 
incidents related to drunk or drowsy driving, 
improving overall ride safety and service quality.},
        keywords = {Driver Monitoring, Alcohol  Detection, Drowsiness Detection, Raspberry Pi,  IoT, Intelligent Transportation Safety, Ride hailing Safety},
        month = {May},
        }