Traffic sign recognition plays a crucial role in the safe and efficient operation of self-driving vehicles. This article presents a MatLab and TensorFlow-based approach for traffic sign recognition in the context of self-driving vehicles. The proposed method leverages deep learning techniques, specifically convolutional neural networks (CNNs), to detect and classify traffic signs. The article outlines the methodology, including the dataset used, pre-processing steps, model architecture, and implementation details. Results demonstrate the effectiveness of the approach, with high accuracy and reliable recognition performance. The study contributes to the advancement of self-driving vehicle technology by providing an efficient and accurate solution for traffic sign recognition.

Traffic sign recognition, self-driving vehicles, deep learning

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