Deep Learning Implementation of Autonomous Driving using Ensemble-M in Simulated Environment

Abstract Making Autonomous Driving a safe, feasible and better alternative is one the core problems the world is facing today. The horizon of the applications of AI and Deep Learning has changed the perspective of the human mind. Initially, what used to be thought as subtle impossible task is applicable today and that too in the feasibly efficient way. Computer vision tasks powered with highly tuned CNNs are outperforming humans in many fields. Introductory implementations of autonomous vehicle were merely achieved using raw image processing and hard programmed rule-based logic systems along with machine/deep Learning used as secondary objective handlers. With the autonomous driving method proposed by NVIDIA, the usability of CNNs is more adequate, adaptable and applicable. In this paper, we propose, the ensemble implementation of CNN based regression models for autonomous-driving. We have taken simulator generated driving view image dataset along with mapped file of steering angle in radians. After applying image pre-processing and augmentation, we have used two CNN models along with their ensemble and compared their performance as to minimize the risks of unsafe driving. We have compared Nvidia proposed CNN, MobileNet-V2 as regression model and Ensemble-M results for comparison their respective performance, MSE scores and compute time to process. In result analysis, the MobileNet-V2 model performs better in densely-featured roads and Nvidia model performs better in sparsely-featured roads whereas Ensemble-M normalizes the performance of both models and efficiently result in the least MSE score (0.0201) with highest computation time utilization.