Accident prevention and safety assistance using IOT and machine learning

Transportation is playing a vital role in our daily life and its development has made many of our chores much easier. But in recent years, driver drowsiness, distractions, and speed limit crossing cause ruinous road accidents which lead to fatalities. Slumbering, dozing, alcohol consumption cause intrusiveness which needs to alert the driver before a mishap happens. In this paper, a prototype is designed using Raspberry Pi, Pi Camera, sensors for monitoring driver’s eye movements, detecting yawning, detecting toxic gases, and alcohol consumption to prevent accidents and provide safety assistance to drivers. Internet of Things and machine learning-enabled system is implemented in vehicles for transmitting the behavior of the driver and his driving pattern to the cloud to take quick response under emergency situations. Several lives are saved by alerting the driver with help of a sound system that is deemed to prevent any distractions before happen. The cloud services and machine learning are employed in identifying fatigue drivers through the collected and stored dataset from cloud services. The device is experimentally tested, and the results show its efficiency and effectiveness. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG part of Springer Nature.