Efficiency study of VGG networks in autonomous driving tasks

In this study, we aim to evaluate the applicability of the VGG 19 architecture-based neural network for autonomousdriving tasks. The objective is to demonstrate the effectiveness of employing this neural network to solve the problemand to compare the identification quality. Experimental methodology is utilized to compare the efficiency of different neural networks. During the experimentation, we observed a disparity in the efficiency of VGG with and without theinclusion of the attention mechanism. Presently, a variety of machine learning methods are increasingly used to addressdiverse problems. Image recognition holds particular significance among these challenges. The practical problemunderinvestigation in this study pertains to the identification of roads with varying curvatures and their correspondence tothevehicle's steering angles for the purpose of autonomous vehicle control. This paper explores multiple types of neural networks commonly used for image recognition. Previous research has demonstrated the high efficiency of these neural networks in solving various tasks, while this evaluation focuses on the applicability of neural networks in solvingtheautonomous driving problems and a performance comparison of these methods. The experimental results indicatethat VGG 19 achieves the highest accuracy of all neural networks.