Task-Aligned Stability Analysis of Vision-Language Models for Autonomous Driving Hazard Detection
arXiv:2606.11889v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly used for scene understanding in autonomous driving, but robustness analysis often relies on task-agnostic embedding stability alone. We study whether corruption-induced embedding drift predicts changes in a task-aligned hazard score derived from CLIP image-text similarities. Using controlled corruptions on BDD100K road scenes, we compare embedding drift against margin drift, defined as the change in hazard score under perturbation. The relationship is highly corruption-dependent: some families exhi
The increasing deployment of Vision-Language Models (VLMs) in safety-critical applications like autonomous driving necessitates rigorous, task-aligned robustness analysis.
Understanding the stability of VLMs under real-world corruptions directly impacts the safety and trustworthiness of autonomous systems, influencing regulatory frameworks and public acceptance.
Robustness evaluations for VLMs are moving beyond generic embedding stability to focus on task-specific performance degradation, providing more actionable insights for deployment.
- · Autonomous driving developers
- · Safety standard organizations
- · AI robustness research
- · Developers solely relying on generic VLM evaluations
- · Companies with less robust AI systems
Improved safety and reliability of VLM-powered autonomous vehicles.
Accelerated adoption and public trust in self-driving technology due to enhanced safety guarantees.
New certification requirements and industry standards for VLM robustness in safety-critical AI applications beyond autonomous driving.
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Read at arXiv cs.AI