Optimizing AI-Based Traffic Sign Recognition in Electric Vehicles with GELU-Activated CNNs

Abstract

Traffic sign recognition is critical for intelligent transportation systems and autonomous driving. Conventional convolutional neural networks (CNNs) typically utilize the ReLU activation function for its computational efficiency; however, alternative activation functions can improve computing effectiveness capacity in recognition tasks. In this study, we propose a CNNs model enhanced with the Gaussian Error Linear Unit (GELU) activation function. We evaluate its performance on benchmark datasets and compare it against both ReLU and Leaky ReLU baseline. Experimental results show that the proposed GELU-activated CNNs achieves a recognition accuracy of 99.75% and provides small but consistent improvements over ReLU and Leaky ReLU models, particularly under challenging conditions such as occlusion and low lighting. These findings highlight GELU’s potential to enhance the robustness and reliability of traffic sign recognition in Electric Vehicles for autonomous driving applications.