Smart streetlight framework for collision prediction of vehicles

Road accidents cause a large number of public fatalities in developing countries affecting them economically. It has been more than a decade since automobile industries are into developing intelligent vehicles with a major objective of road safety. In order to warn or assist the drivers, various automobile manufacturers have been focusing on developing autonomous or semi-autonomous vehicles, but not much effort has been drawn towards developing the smart infrastructure to supplement the development of intelligent vehicles. This paper proposes a framework for a smart vehicle monitoring system to provide smooth mobility of vehicles using roadside units such as streetlights. Such a system aims to eliminate the necessity of equipping each vehicle with smart sensors, which is typically an expensive scheme in most of the developing and underdeveloped countries around the world. This paper attempts to approach the problem of vehicle collision prediction by designing the system based on the concepts from science and engineering such as relative motion and vectors and implementing it using a machine learning-based model. These concepts led to the generation of datasets over which the prediction model was trained. The performance of the proposed model has been simulated using the software - Virtual Crash.