HST-HGN: Heterogeneous Spatial-Temporal Hypergraph Networks with Bidirectional State Space Models for Global Fatigue Assessment
arXiv:2604.08435v2 Announce Type: replace-cross Abstract: It remains challenging to assess driver fatigue from untrimmed videos under constrained computational budgets, due to the difficulty of modeling long-range temporal dependencies in subtle facial expressions. Some existing approaches rely on computationally heavy architectures, whereas others employ traditional lightweight pairwise graph networks, despite their limited capacity to model high-order synergies and global temporal context. Therefore, we propose HST-HGN, a novel Heterogeneous Spatial-Temporal Hypergraph Network driven by Bidi
The continuous improvement in AI models and computational efficiency allows for real-time applications like driver fatigue assessment to become more viable and widespread.
Advanced and efficient models for real-time monitoring, such as driver fatigue, will enhance safety in transportation and industrial settings, impacting insurance, logistics, and regulatory frameworks.
The ability to accurately assess subtle human states from video with constrained computational budgets could accelerate the deployment of AI-driven safety systems in edge devices.
- · AI model developers
- · Transportation companies
- · Autonomous vehicle developers
- · Insurance providers
- · Developers of computationally heavy AI architectures
Improved real-time driver fatigue detection systems will enhance road safety and reduce accidents.
The widespread adoption of such systems could lead to new regulations for commercial drivers and a shift in insurance pricing models.
This technology might eventually extend to general human state monitoring in critical roles, impacting productivity and safety across various industries.
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Read at arXiv cs.AI