This paper proposes a comprehensive IoT and AI-based smart vehicle accident prevention and monitoring system. The system integrates multiple sensors including ultrasonic, MEMS accelerometer, vibration sensor, MQ-3 alcohol sensor, temperature sensor, and tyre pressure sensor to monitor various vehicle and driver parameters in real time. The core innovation lies in the integration of AI-powered driver emotion detection using Python image processing, which can identify risky emotional states such as anger, stress, or drowsiness. The data from the sensors and the camera are processed to generate alerts, trigger emergency responses, or even stop the vehicle when dangerous conditions are detected. The IoT framework enables remote monitoring and real-time updates, making the system more effective in emergency situations. By combining environmental monitoring, vehicle health diagnostics, accident impact detection, and human emotion analysis, the proposed system offers a unique multi-layered safety solution. This integration significantly reduces the chances of accidents caused by mechanical faults, drunk driving, poor vehicle maintenance, or driver emotions, thereby improving overall road safety.
G et al. (2026) studied this question.