EyeCue: Driver Cognitive Distraction Detection via Gaze-Empowered Egocentric Video Understanding
Published in Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence (IJCAI 2026), 2026
Abstract: Driver cognitive distraction is a major cause of road collisions and remains difficult to detect. Unlike manual or visual distraction, cognitive distraction is diverted by thoughts unrelated to driving, even when the driver appears visually attentive and exhibits no explicit physical movements. In this work, we propose EyeCue, a gaze-empowered egocentric video understanding framework, to detect driver cognitive distraction. A key insight is that cognitive distraction manifests in the interaction between eye gaze and visual context. To capture this interaction, EyeCue integrates eye gaze with egocentric video to enable context-aware modeling of the driver’s attention over time. Furthermore, to tackle the limited scale and diversity of existing datasets, we introduce CogDrive, a comprehensive multi-scenario dataset that augments four existing driving datasets with cognitive distraction annotations. Through extensive evaluations on CogDrive, we show that EyeCue achieves the highest accuracy of 74.38%, outperforming 11 baselines from 6 model families by over 7%.
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Recommended citation: L. Zhang, J. Yoon, M. Corbett, A. Sarkar, and B. Ji, “EyeCue: Driver Cognitive Distraction Detection via Gaze-Empowered Egocentric Video Understanding,” in Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence (IJCAI 2026).