Intelligent Transportation using Deep Learning

Abstract

The goal of this paper is to advance intelligent transportation program through the creation of a data collection system, a Convolutional Neural Network (CNN) model for intelligent transportation, and a simulator to test the trained CNN model. The data collection system collects data from a vehicle- steering wheel angle, speed, and images of the road from three separate angles at the time of the data collection. A CNN model is then trained with the collected data. The trained CNN model is then tested on a simulator to evaluate its effectiveness.