Applying deep convolutional neural network (DCNN) algorithm in the cloud autonomous vehicles traffic model

Abstract

Connected and Automated Vehicles (CAVs) is an inspiring technology that has an immense prospect in minimizing road upsets and accidents, improving quality of life, and progressing the effectiveness of transportation systems. Owing to the advancements in the intelligent transportation system, CAV plays a vital role that can keeping life lively. CAV also offers to use to transportation care in producing societies protected more reasonable. The challenge over CAV applications is a new-fangled to enhance safety and efficiency. Cloud autonomous vehicles rely on a whole range of machine learning and data mining techniques to process all the sensor data. Supervised, Unsupervised, and even reinforcement learning are also being used in the process of creating cloud autonomous vehicles with the aim of error-free ones. At first, specialized algorithms have not been used directly in the cloud autonomous vehicles which need to be trained with various traffic environments. The creation of a traffic model environment to test the cloud autonomous vehicles is the prime motto of this paper. The deep Convolutional Neural Network (CNN) has been proposed under the traffic model to drive in a heavy traffic condition to evaluate the algorithm. This paper aims to research an insightful school of thought in the current challenges being faced in CAVs and the solutions by applying CNN. From the simulation results of the traffic model that has traffic and highway parameters, the CNN algorithm has come up with a 71.8% of error-free prediction.