Advanced Automobile Safety Module

This paper introduces an Advanced Automobile Safety Module that integrates Internet of Things (IoT), artificial intelligence (AI), and computer vision to enhance vehicle safety and accident prevention. The system is built on a microcontroller-based platform, incorporating blind spot detection, collision warning, alcohol detection, voice command assistance, drowsiness detection, traffic sign recognition, tire pressure monitoring, NFC-based access control, and a black box data recorder. By leveraging computer vision and deep learning models, the system detects driver fatigue, recognizes traffic signs, and monitors vehicle surroundings to prevent potential collisions. Real-time data from sensors is processed using machine learning algorithms to ensure precise decision-making, while NFC authentication and cloud-based data storage enhance security and remote monitoring. Successful detection of hazards prompts immediate alerts and automated corrective actions, ensuring driver and passenger safety. Performance evaluations indicate high accuracy in detecting road hazards, drowsiness, and alcohol levels, demonstrating the system’s effectiveness. Future advancements will focus on cloud-based AI analytics, vehicle-to-vehicle communication, and adaptive driving assistance for further improvements. Keywords—Automobile Safety, Collision Prevention, Driver Monitoring, Artificial Intelligence, Internet of Things (IoT), Computer Vision