Risk-Aware Selective Multimodal Driver Monitoring with Driver-State World Modeling
Continuous driver monitoring in automated vehicles requires low-latency inference while avoiding unsafe decisions under uncertain driver states. Large vision-language models provide broad multimodal priors, but their latency and limited reliability in this setting make them unsuitable as always-on in-cabin monitors. We propose a cost-aware selective inference framework for deployable multimodal driver monitoring. The core system is a lightweight RGB-physiological student that combines in-cabin visual observations with window-level HR/EDA signals, and a learned gate that decides when to accept the fast prediction or abstain for safety intervention. Additional controls show that the learned...
| Authors | Daosheng Qiu, Haozhuang Chi, Hao Su et al. |
|---|---|
| Primary category | cs.RO |
| Published | 25 June 2026 |
| Last updated at source | 25 June 2026 |
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