Advancing Road Sign Recognition in Autonomous Vehicles Through Convolutional Neural Networks: Methods and Outcomes

Abstract

With the rapid advancement of autonomous driving technology, road sign recognition has become a critical component in ensuring the safety of autonomous vehicles. This paper presents a method for road sign recognition in autonomous vehicles using Convolutional Neural Networks (CNN), with the goal of enhancing both accuracy and efficiency in road sign detection. Initially, the background and significance of this issue are discussed, including the progression of autonomous vehicle technology and the necessity for reliable road sign recognition systems. The paper emphasizes the role of convolutional neural networks in this application. A comprehensive overview of the dataset employed, the architecture of the designed CNN model, and the data preprocessing techniques are provided. To further enhance accuracy, techniques such as data augmentation and image enhancement were utilized. The findings of this research demonstrate that the proposed CNN-based road sign recognition method achieves high accuracy and efficiency, thereby offering substantial support for the practical implementation of autonomous driving technology.