Advanced Driver Assistance Systems for Driver Health and Fatigue Monitoring Using Camera, Biometric Sensors, Telematics and Machine Learning

<div class="section abstract"><div class="htmlview paragraph">Automotive industries focus on driver safety leading to raising improvements and advancements in Advanced Driver Assistance Systems (ADAS) to avoid collisions and provide safety and comfort to the drivers. This paper proposes a novel approach toward Driver health and fatigue monitoring systems that uses cabin cameras and biometric sensors communicating continuously with vehicle telematics systems to enhance real-time monitoring and predictive intervention. The data from the camera and biometric sensors is sent to the machine learning algorithm (LSBoost) which processes the data and if anything is wrong concerning the driver's behavior then immediately it communicates with vehicle telematics and sends information to the emergency services. This approach enhances driver safety and reduces accidents caused due to health-related driver impairment. This system comprises several sensors and fusion algorithms are applied between different sensors like cabin camera and biometric sensors, all these sensors are placed inside the driver compartment without disturbing the driver's comfort and functionality. The input from all these sensors is feed to a centralized processing unit, where advanced sensor fusion technology and machine learning algorithms are used for processing the raw data information. Especially selective machine learning models are used to detect patterns indicative of driver behavior such as drowsiness, fatigue, etc. The alerts will initially notify the driver. If the driver doesn’t respond, the alerts will then be forwarded to the vehicle’s telematics system, which in turn will notify emergency services like ambulance, or designated contacts.</div><div class="htmlview paragraph">This paper presents the architecture of the driver health monitoring system and the effectiveness of the proposed system is validated to the simulated model in the MATLAB environment showcasing its potential and ability to significantly enhance driver safety.</div></div>