Can AI help prevent road accidents by detecting driver emotions? This paper introduces SVGG, an emotion recognition model that leverages the attention mechanism. It proposes an emotion recognition model that leverages the attention mechanism. We validate our approach through comprehensive experiments on two distinct datasets, assessing the model’s performance using a range of evaluation metrics. The study addresses the problem of unstable emotions, particularly anger, which contribute to traffic accidents. To address this issue, driver emotion recognition emerges as a promising solution within the realm of cyber-physical-social systems (CPSS). In this paper, we introduce SVGG, an emotion recognition model that leverages the attention mechanism. We validate our approach through comprehensive experiments on two distinct datasets, assessing the model’s performance using a range of evaluation metrics. The SVGG model aims to improve road safety by identifying and responding to a driver’s emotional state in real time. The results suggest that the proposed model exhibits improved performance across both datasets. This technology promises to enhance safety and driver-assistance systems. The SVGG model, validated on two datasets, shows improved performance, potentially revolutionizing driver assistance and traffic safety.
Published in Frontiers in Physics, this article aligns with the journal’s focus on cutting-edge research in physics and related interdisciplinary fields. By applying an attentional convolutional network to driver emotion recognition, the study bridges physics and engineering with social systems, fitting well with the journal’s scope and emphasis on innovative applications of physical principles.