Design and Implementation of a Safe Driving System for Real-Time Driver Behavior Analysis and Hazard Alerting Using Low Cost, Universally Compatible Embedded Hardware

This research presents the development of the Safe Driving System (SDS), given name AuraGuard, a cost effective, universally compatible embedded solution aimed at proactively reducing road accidents and enhancing driver safety. The SDS system runs on a Raspberry Pi system with Python and open-source tools like OpenCV and Dlib. It includes 13 features that watch for driver fatigue, alcohol use, phone use, speed, and driving assist in poor visibility. The system tracks environmental conditions and driver actions in real time using sensors and artificial intelligence. It sends feedback through various sensory channels to notify users. SDS works with any vehicle type and operates at low cost while adjusting to different areas with limited resources. The system underwent thorough testing in both lab based and actual driving environments across different vehicle models. Most system features achieved accuracy rates above 90% and Access Control and Overspeed Detection performed almost flawlessly. SDS helps improve road safety through its integrated use of driver help systems and emergency response technology. This paper contributes to the field of intelligent transportation by demonstrating how multi-sensor, AI enhanced systems can shift the paradigm from reactive protection to proactive prevention, thereby significantly reducing road accident risks.