From Opacity to Clarity: Employing Explainable AI to Interpret CNN Predictions on Winter Road Conditions
Mingjian Wu, Tae Jung Kwon · Journal of Transportation Engineering Part A Systems · 2025
The development of deep learning models for winter road surface conditions (RSC) classification has advanced in recent years. However, most of these models remain nontransparent, limiting confidence in their predictions. This study is a pioneering effort that employs two explainable artificial intelligence (XAI) techniques, gradient-weighted class activation mapping (Grad-CAM) and Shapley additive explanations (SHAP), to clarify the processes through which convolutional neural networks (CNNs) interpret winter RSC imagery. Grad-CAM provides visual explanations by highlighting important regions in the images, while SHAP offers numerical evaluations of feature importance and identifies features with negative contributions. Such scrutiny is vital both practically and methodologically as it offers transparency and facilitates more reliable artificial intelligence (AI) integration into RSC monitoring. Our investigation focuses on three components: CNN’s attention to relevant image features, the influence of training data size, and the impact of varying CNN architectures. The findings demonstrate that CNNs classify RSC imagery by identifying critical features, such as the road surface portion of the image, and that an increased number of training samples enhances learning capabilities. The study further confirms that different architectures might also affect prediction performance. By unveiling the internal decision-making processes of CNNs, this study addresses the transparency gap and contributes to more effective and informed winter road maintenance operations. The integration of XAI techniques ensures that AI models are not only accurate but also interpretable, enhancing their practical applicability. These insights not only reinforce trust in AI’s real-world applications but also deepen understanding of CNNs, thereby encouraging the development of transparent and reliable AI models, particularly for improving winter transportation safety and mobility.