Internet of Things in Cloud Computing using Light Fidelity for Autonomous Vehicles

Abstract

Modern intelligent transportation systems remain in the newlineevolution stage owing to the complexity of their multiple factors comprising newlinethe control of urban roads, pedestrians, and vehicles. Also, intelligent driving newlinesupport systems are recommended according to the active safety principle newline newlineAutonomous Vehicles (AVs) are elements of intelligent transportation that newlinestarting to emerge on commercial roads and travelling towards full newlineautonomy. AVs can save millions of lives and enhance the efficiency of newlinetransportation services. In order to drive safely and effectively, AVs rely on newlinesensors like cameras and Light Detection and Ranging (LiDAR) to monitor newlinethe road. However, with the existence of other AVs on the road, they can newlineimprove their strengths through Vehicle-To-Vehicle (V2V) and Vehicle-To newlineInfrastructure (V2I) communication. By using V2V and V2I communication, newlineflow and increasing road safety. Recent advances in the fields of Artificial newlineIntelligence (AI) and Deep Learning (DL) in particular have led to newlineforecasting traffic and avoiding congestion during AV communication. newlineHowever, two important aspects such as consistency and reliability were newlinefailed to efficiently address for maintaining the safety and security of newlineautonomous vehicles. This leads to a weakness for autonomous vehicles and newlineprone to numerous security and safety issues. Also, the end-to-end delay and newlinecollision rate were not reduced in the communication process. To solve such newlineissues, novel and efficient methods are developed in this research work for newlineaccurate traffic forecasting and to ensure safe AV communication. newline newline