Abstract
The system is Cross-Platform Mobile Application using Face Detection with Machine learning detecting fatigue of driver, which will help in preventing road mishaps. Every year road accidents due to human error cause an increasing amount of deaths and injuries globally. Driver’s drowsiness is recognized as an important factor in vehicle accidents. Preventing drowsiness among drivers is a necessity to get an accurate result about the driver's fatigue level and provide a warning to the driver to help them avoid drowsiness and ensure safe driving not only for them but also for others who are in the vicinity of the vehicle area while driving. This app offers a method for scanning the facial landmarks and after detecting the face use the required landmarks for eye tracking. This will allow the vehicle to be in complete control of the driver. The system uses the front camera of the mobile phone that points towards the driver’s face or the dashboard camera and monitors the driver’s face to detect fatigue. In case, the drowsiness is detected an alarm is used to warn the driver and the alarm is then switched off manually with an idea that the alarm will be providing an alert until the driver is wakeful. For this purpose, a deactivation button will be used to deactivate the alert or alarm. There will be more features in the system such as sending SOS if something happens to a vehicle at an isolated place. The app also provides helpline numbers, in case of any emergency situation the driver can contact the required authorities as and when required. The app comes with a detailed list of road safety measures to remind the driver about the rules and regulations to be followed for a safe ride. Vivify can also be used for navigation purposes using the map feature in the app. Flutter will be used for a native and user-friendly interface of the system. This will allow the app to be available for both android as well as IOS devices. Minimal use of the hardware will ensure smooth processing. This system can be used for different scenarios like for cab services, on-road cargo service, and late-night travelers.
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