An Automated System for Speed Control and Accident Prevention based on Multiple Parameters

With the simultaneous evolution of Internet of things (IoT) and 5G technology, the connectivity between different devices has been increased. This resulted in automation of number of applications spread over wide areas with accurate decision making due to the advent of machine learning algorithms. The statistical data available is being used to train a model there by reducing the human involvement. Among the number of areas, development of driver less cars are the most tending area these days. The fundamental problem faced in the design is to avoid collision and prevent accidents. Even though there is a steep rise in the automobile market, the rise in the number of casualties is becoming concern day by day. Irrespective of dissemination of safety measures, the reluctance of the drivers is also increasing following the same. Road accidents occur majorly due to the consumption of alcohol or sometimes due to overstraining due to continuous driving and health conditions. Lot of research has been done on monitoring the physical and health conditions of drivers in vehicles by integrating inputs from sensors. However, the existing works lack uniqueness in the sense that all the conditions needed to prevent accident as well as prevention of injuries at the outset of accidents are not incorporated. This indicates the lack of sensing elements that can perform multiple tasks. The current work is aimed at development of prototype indigenous system that can monitor the state of the driver as well as control the speed of the vehicle at the onset of a deviation in the driver’s body parameters. The location of the vehicle can be continuously tracked and if any accident happens, information will be conveyed is conveyed to concerned persons