Traffic Signal Classification using Deep Learning for Enhanced Autonomous Driving Systems

Abstract

This paper presents a deep learning-based model for the classification of traffic signals, a crucial component for the development of autonomous driving systems and driver alert mechanisms. Utilizing a convolutional neural network (CNN), the proposed model is designed to recognize 43 distinct traffic signs with high accuracy. The model is trained on a publicly available dataset, achieving an impressive accuracy of over 95% in just 50 epochs. The preprocessing steps, model architecture, and training process are discussed in detail. The model's performance indicates its potential for real-world applications in enhancing the safety and efficiency of self-driving cars. Future work includes hyper parameter tuning and the integration of the model into real-time systems for further optimization.