A Deep Learning Approach Towards Building Intelligent Transport System

The automobile has typically served as the man’s ambulatory system’s by responding to the driver’s directives. This concept has been altered by recent improvements in embedded systems, communications, and controls, opening the way for the Vehicle system which is intelligent. The vehicle has evolved into a powerful sensor platform, gathering information from the environment, other vehicles, and the vehicle itself, and sending it to the driver and infrastructure to aid in safe navigation, pollution control, and traffic management. Intelligent Vehicles are the next phase in this evolution. The Internet of Vehicles, which was pioneered by the Google automobile, will be a distributed transportation system capable of making its own decisions about driving users to their destinations. The concept that will help transition to the intelligence of Vehicles is the Vehicular Cloud, providing all the services required by autonomous vehicles. This paper proposes an Intelligent Transport System (ITS) model to detect the environment and classify them into different categories. So, the driver will understand the severity of the current situation and take the correct decision. The proposed model mainly detects the signboards, static and moveable objects on the road and classifies them according to the different categories. The proposed model predicts the action and suggests it to the Automated Vehicle (AV) along with the object detection. This may help the driver to do cautionary driving.