arXiv:2601.08467v2 Announce Type: replace-cross Abstract: Distracted driving is a major cause of traffic collisions, calling for robust and scalable detection methods. Vision-language models (VLMs) enable strong zero-shot image classification, but existing VLM-based distracted driver detectors often underperform in real-world conditions. We identify subject-specific appearance variations (e.g., clothing, age, and gender) as a key bottleneck: VLMs entangle these factors with behavior cues, leading to decisions driven by who the driver is rather than what the driver is doing. To address this, we
The proliferation of advanced vision-language models makes their application to real-world safety critical issues like distracted driving detection both feasible and necessary for improvement.
Improving the accuracy and reliability of AI for safety-critical applications like distracted driving has immediate implications for public safety, insurance, and the autonomous vehicle industry.
This research outlines a method to make distracted driver detection more robust by actively decoupling irrelevant subject variations from behavioral cues, enhancing VLM performance in complex real-world scenarios.
- · Automotive safety systems
- · Insurance companies
- · Autonomous vehicle developers
- · Drivers (due to increased safety)
- · Manufacturers of less sophisticated detection systems
More accurate distracted driver detection systems could be integrated into new vehicle models.
Insurance premiums might be adjusted based on driver monitoring data, incentivizing safer driving.
The technology could evolve into broader in-cabin monitoring for various safety and comfort applications, moving beyond just distraction detection.
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Read at arXiv cs.LG