An Intelligent IoT-Based Framework for Enhancing Safety and Monitoring in Smart Automotive Vehicles

This study presents a comprehensive intelligent vehicular safety and monitoring system designed within the Internet of Things (IoT) paradigm to enhance real-time driver and vehicle condition assessment. The proposed system integrates a heterogeneous network of sensors including alcohol detection, eye-blink monitoring, accelerometers, and GPS/GSM modules with edge-computing-enabled microcontrollers to provide continuous, real-time situational awareness. Leveraging machine learning algorithms, the system improves detection accuracy for critical events such as driver inebriation, fatigue-induced micro-sleep, and collision impacts, while minimizing false alarms in dynamic operational environments. The architecture addresses key challenges such as sensor drift, noise filtering, and context-aware processing within constrained hardware resources. Automatic alerting mechanisms facilitate timely emergency response, while telematics connectivity supports remote monitoring and vehicle theft prevention. The modular design and cost-effective hardware choices position this solution as viable for both commercial fleet management and private passenger vehicles, offering a scalable approach to intelligent transportation system integration and autonomous vehicle ecosystems.