Intelligent Traffic Control System using Deep Reinforcement Learning

Abstract

Abstract: Road Traffic signals are placed in particular positions to make sure the protection of vacationers. In this paper, we proposed a device for site visitors signal detection and recognition, in addition to a technique for extracting an avenue signal from an herbal complicated photo for processing and alerting the driving force via voice command. The reliability of the device is increased through different aspects such as noise, partial or absolute underexposure, partial or whole overexposure, considerable versions in shadeation saturation, extensive sort of viewing angles, view depth, and shape/shadeation deformations of site visitors symptoms etc. The proposed structure is sectioned into three phases. The first of that is photo pre-processing, where the dataset`s enter files are quantified, which decides the enter length for getting to know purposes, and resizes the records for the getting to know step. A Convolutional Neural Network (CNN) is used to train within the segment side which further more offers the text-to-speech translation, with the detected signal from the second one segment being supplied in audio format, which demonstrates better accuracy.