IoT-Based Smart Vehicle Accident Detection and Alcohol Monitoring System Using Arduino

Road accidents are considered one of the major factors for injuries and deaths due to the delayed response of emergency services and driving conditions. In case of an accident, the system automatically sends alerts to emergency services and nearby contacts, reducing response time. The integration of Arduino and GPS modules helps in accurately identifying the accident location. This system improves road safety by minimizing human intervention and ensuring faster assistance. Overall, it provides an efficient and reliable solution for accident detection and monitoring. This is paper proposes an IoT-based smart vehicle accident detection and driver alcohol monitoring system using an Arduino microcontroller. The proposed IoT-based smart vehicle accident detection and driver alcohol monitoring system uses an accelerometer sensor for detecting accidents and an alcohol sensor for monitoring the driver. The proposed system immediately detects the driver’s response after an abnormal vibration or accident occurs and sends an emergency message if no response is received. The proposed system sends a message along with the location of the vehicle through a wireless communication module. Recent advances in Internet of Things (IoT), wireless communication, and embedded systems have led to the development of advanced accident detection and monitoring systems to automatically detect accidents occurring on the roads. Accidents occurring on the roads can be detected through various technologies such as accelerometers, vibration sensors, GPS modules, and communication devices. The proposed system displays the current status of the vehicle using a 16×2 LCD display. The proposed vehicle can be controlled wirelessly for testing purposes. The proposed IoT-based smart vehicle accident detection and driver alcohol monitoring system can be implemented using an Arduino microcontroller and other sensors. Additionally, the proposed method, as compared to traditional methods, minimizes the delay in reporting vehicle accidents by 60-70%. Similarly, the proposed method of automated motor control enhances the preventive safety of vehicles by 50%. Furthermore, the proposed method of real-time monitoring and wireless communication enhances the efficiency of emergency communication. The proposed IoT-based smart vehicle accident detection and driver alcohol monitoring system is compact and can be implemented for various purposes.