Author:
Dr. Mallikarjun M Kodabagi ,Afrah Ayub ,Mahima BK ,Shruthi Siva ,Apurva Korni
Abstract
The rise in accidents caused by drivers who are too sleepy to drive has made advanced driver alert systems necessary. This research proposes a novel method for identifying drowsy drivers through the integration of GPS technology and physiological data, offering an inventive solution to this pressing problem. We explore the complexities of drowsiness detection, highlighting the difficulties and introducing an improved approach to improve performance. Using real-time GPS data, our system not only warns drivers when something is wrong, but it also suggests appropriate rest spots depending on the driver's present location. Our method, which emphasizes proactive measures to limit the risks associated with drowsy driving, pioneers safer driving habits by seamlessly combining physiological measurements, GPS technology, and algorithmic enhancements. Performance assessments show encouraging outcomes, highlighting the potential advantages and importance of these devices in lowering driver fatigue-related incidents. This research advances vehicle safety and emphasizes the need of taking preventative action to reduce the risks associated with sleepy driving.
Reference13 articles.
1. Chalermkiat Chanachan, Patthachaput Thanesmaneerat, Thanrada Mahasukon, Jumpol Povichai " Car Driver’s Behaviors Detections using Ensemble Model" in IEEE; <2023 ITC-CSCC, DOI: 10.1109>, March 2023.
2. Binyan Zhang, “Design of Face Recognition Fatigue Driving Detection System Based on Improved YOLO Algorithm” ICMIII, July 2023.
3. Afsar P, Dr Shanid Malayil, Aasha Verghese, Aysha Sithara, Amithab Pakarath, Bijoy, Ashmin K T “Real Time Student Emotion and Drowsy Detection using yolo v5 and CNN for Enhanced Learning ICSP, July 2023.
4. Li Lou, Tiantian Yue “Fatigue Driving Detection Based on Facial Features” ICSP, May 2023.
5. Jinzhao Zhang “Human-computer Interaction for Driver Fatigue Detection through Micro-expressions”, ICSECE, April 2023