“SMART AUTOMOTIVE MONITORING SYSTEM FOR ACCIDENT & DROWSINESS DETECTION USING IoT & SENSOR NETWORKS”

Aiming to significantly reduce accident rates and mitigate the risks associated with driver fatigue, this paper presents a Smart Automotive Monitoring System (SAMS) that effectively addresses these challenges by leveraging the Internet of Things (IoT) and advanced sensor networks.The paper highlights the critical issue of driver drowsiness and the growing frequency of road accidents.To address these issues, SAMS integrates a multi-sensor approach to monitor real-time vehicle and driver conditions.A network of sensors, including but not limited to cameras, accelerometers, gyroscopes, and biometric sensors, is the fundamental component of SAMS.These sensors are positioned carefully to keep an eye on a number of variables, including lane position, driver attention, and vehicle speed.The data gathered from these sensors is analyzed by the system using machine learning algorithms and computer vision techniques.SAMS combines eye-tracking and facial recognition technology to assess indicators of weariness in order to detect drowsiness.The driver can then be alerted by the system using visual and audio cues, or in more serious situations, it can start emergency procedures on its own to guarantee safety.The Internet of Things (IoT) framework enables smooth data transfer between sensors and a central processing unit, which is frequently located in the onboard computer of the car.Real-time data processing is supported by this architecture, and predictive analytics are made possible, allowing for the early detection of possible risks.In order to save data and monitor it remotely, the system is also connected to cloud services.This enables historical analysis and updates to enhance performance over time.