Control of an Autonomous Vehicle with Obstacles Identification and Collision Avoidanceusing Multi View Convolutional Neural Network

Abstract

Artificial Intelligence (AI) is inevitable in this era for automation requirements in all largescale industries like Automotive, Aerospace, Railways, Industrial automation, and Renewable energy industries. Among the AI techniques, deep learning algorithm with artificial neural network (ANN) receives greater attention on estimation and control requirements. In this paper, control of autonomous passenger vehicle using deep multi view convolutional neural network (CNN) for the identification of obstacles with 3 dimensional (3D) images of the same using Winograd Minimal filter algorithm (WMFA) has been presented. Authors also have clearly articulated the accuracy level difference between Machine learning (ML) algorithm, basic CNN algorithm and the proposed CNN algorithm in this paper forobstacle identification, collision avoidance and steering control. Most importantly, training of neural networks with multi view topology using Matlab/Simulink coding has been presented with the results. Real-time 3D images have been captured and compared with the stored and trained data. Output of trained CNNs have been captured and the results have been compared and discussed in this paper.