LNet: Lightweight Network for Driver Attention Estimation via Scene and Gaze Consistency

In resource-constrained vehicle systems, establishing consistency between multi-view scenes and driver gaze remains challenging. Prior methods mainly focus on cross-source data fusion, estimating gaze or attention maps through unidirectional implicit links between scene and facial features. Although bidirectional projection can correct misalignment between predictions and ground truth, the high resolution of scene images and complex semantic extraction incur heavy computational loads. To address these issues, we propose a lightweight driver-attention estimation framework that leverages geometric consistency between scene and gaze to guide feature extraction bidirectionally, thereby strengthening representation. Specifically, we first introduce a lightweight feature extraction module that captures global and local information in parallel through dual asymmetric branches to efficiently extract facial and scene features. An information cross fusion module is then designed to promote interaction between the scene and gaze streams. The multi-branch architecture extracts gaze and geometric cues at multiple scales, reducing the computational redundancy caused by mixed features when modeling geometric consistency across both views. Experiments on a large public dataset show that incorporating scene information introduces no significant computational overhead and yields a better trade-off between accuracy and efficiency. Moreover, leveraging bidirectional projection and the temporal continuity of gaze, we preliminarily explore the framework's potential for predicting attention trends.