arXiv:2606.26922v1 Announce Type: cross Abstract: 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 signal
The increasing sophistication of AI models, coupled with growing industry focus on safety and regulatory pressures in autonomous driving, necessitates more reliable and efficient driver monitoring solutions.
This development addresses a critical safety and scalability bottleneck for autonomous vehicle deployment by enabling more efficient and reliable in-cabin monitoring, reducing the computational overhead of large models.
The shift towards selective, cost-aware inference frameworks allows for the practical implementation of robust multimodal driver monitoring systems in automated vehicles, moving beyond always-on, high-latency models.
- · Automotive OEMs
- · Autonomous vehicle software developers
- · AI hardware manufacturers (edge computing)
- · Regulatory bodies focused on automotive safety
- · Developers of inefficient 'always-on' large AI models for in-cabin monitoring
- · Companies relying solely on single-modality driver monitoring systems
Improved safety and passenger trust in partially or fully autonomous vehicles will accelerate their adoption and deployment.
This selective inference methodology could be adapted to other safety-critical AI applications, optimizing resource use and enhancing reliability.
The established reliability of these systems may reduce insurance premiums for autonomous vehicles and influence future accident liability frameworks.
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Read at arXiv cs.AI