ArticleSensors (Basel, Switzerland)2026
Adaptive Multi-Temporal Fusion and Cross-Modal Adversarial Alignment for Robust Driver Fatigue Detection.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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3 authors.
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Abstract
To address the critical challenges of multi-scale temporal dynamics and sensor-intrusiveness in driver fatigue detection, this paper proposes the Multi-Temporal Fusion Attention Network (MTFA-Net). The framework integrates two core innovations: a Multi-scale Temporal Adaptive Fusion (MTAF) module that dynamically weights short-, mid-, and long-term behavioral features via a scene-aware modulator, and a Physiological-Behavioral Cross-modal Adversarial Alignment (PBCAA) network that implicitly infers latent physiological states (e.g., HRV) from facial videos using adversarial learning and mutual information maximization. Experimental results on RLDD and NTHU-DDD datasets demonstrate that MTFA-Net achieves state-of-the-art accuracy (92.8%) while maintaining high interpretability and real-time efficiency, providing a robust, non-intrusive solution for intelligent cockpit safety.
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Registered trials
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