Towards enhanced situation awareness in HMI for traffic operations: a robust eye-tracking-based method
Xiaoqing Yu, Xing Yao, Chun-Hsien Chen, Ximing Zhou, Bufan Liu · Journal of Engineering Design · 2026
With the rapid increase of automation and intelligent assistance in modern traffic operations, human–machine interaction (HMI) has become central to system effectiveness and operational safety. In such high-stakes environments, operators often need to manage complex information flows and dynamic decision-making processes, making situation awareness (SA) a critical determinant of performance. However, the growing reliance on automated systems raises the risk of SA degradation, potentially undermining safety and efficiency. Eye-tracking has emerged as a promising tool to monitor operator SA, but in real-world traffic control environments, data incompleteness and signal loss remain major obstacles. To address this challenge, we introduce a robust eye-tracking-enabled SA recognition framework, the Masked AutoEncoder for EYE-tracking data (MAEYE). MAEYE integrated CNN modules and Transformer layers to effectively capture both structural patterns and temporal dynamics of eye movements. Leveraging a self-supervised learning paradigm, it demonstrates strong resilience against incomplete data, outperforming state-of-the-art methods under varying levels of data loss. An SA-probe experiment with 26 participants validated its effectiveness, confirming reliable and accurate SA decoding from imperfect gaze input. By enabling dependable SA monitoring in traffic operations HMI, this work advances the development of safer, more resilient, and human-centred automation systems for future mobility and transportation management.HighlightsA robust eye-tracking–based approach is proposed for SA recognition in traffic operations.Self-supervised learning enhances representation robustness under noisy or incomplete eye-tracking inputs.Adaptive masking strategies in autoencoder training are systematically evaluated for robust feature learning.The method consistently outperforms state-of-the-art baselines across multiple evaluation metrics.Reliable SA recognition supports adaptive HMI for safer traffic management.