Alert System for Enhanced Safety Using Machine Learning-Based Fatigue Monitoring

Abstract

Fatigue Monitoring and Alert Systems for drivers play a pivotal role in enhancing road safety by addressing the dangers associated with driver drowsiness and fatigue. These systems employ an array of technologies, including facial recognition, eye-tracking, heart rate monitoring, and vehicle behavior analysis, to continuously evaluate a driver's alertness in real-time. By detecting signs of fatigue such as frequent blinking, yawning, or erratic driving behaviors, the system can accurately identify when a driver is at risk of fatigue. Upon detection, it triggers visual, auditory, or haptic alerts to warn the driver, prompting them to take necessary precautions, like taking a break. Additionally, machine learning algorithms can be utilized to personalize detection based on individual driving patterns, thereby improving accuracy. These systems greatly reduce accidents caused by human error, especially in long-distance driving, commercial transport, and high-risk conditions. This paper discusses the design, functionality, and benefits of fatigue monitoring systems while addressing challenges in widespread adoption and exploring the future potential of this technology in autonomous vehicles and intelligent transportation systems.