arXiv:2606.08123v1 Announce Type: cross Abstract: Vision-based driver monitoring systems are increasingly deployed in safety-critical intelligent transportation settings, yet they are almost always compared on classification accuracy alone. This paper argues that accuracy is insufficient to characterize a model's fitness for real-world deployment, and proposes the Human-Centered Benchmarking Framework (HCBF), which evaluates models across four dimensions: accuracy, explainability, efficiency, and robustness. The framework is applied to four representative lightweight architectures, MobileNetV3
The increasing deployment of vision-based driver monitoring systems necessitates a more robust evaluation framework beyond simple accuracy, as these systems mature and integrate into safety-critical applications.
A comprehensive benchmarking framework for driver monitoring models is crucial for ensuring the safety and reliability of autonomous and assisted driving systems, moving beyond superficial metrics to real-world performance.
The focus for evaluating driver monitoring systems shifts from single-metric accuracy to a multi-dimensional assessment incorporating explainability, efficiency, and robustness, influencing research and development priorities.
- · AI safety researchers
- · Automotive OEMs
- · Driver monitoring system developers
- · Regulatory bodies
- · Developers focused solely on accuracy metrics
- · Companies with opaque AI models
The industry adopts more rigorous testing protocols for AI models in transportation.
There is an increased demand for AI models that are inherently explainable, efficient, and robust, leading to new architectural innovations.
Public trust in intelligent transportation systems grows due to enhanced safety and transparency standards, accelerating adoption of higher levels of autonomy.
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Read at arXiv cs.AI