Task-Aligned Stability Analysis of Vision-Language Models for Autonomous Driving Hazard Detection

Researchers demonstrate that embedding stability alone is insufficient for assessing vision-language model robustness in autonomous driving. Their analysis reveals that corruption-induced representation drift doesn't reliably predict task-specific hazard detection failures, with different corruption types producing asymmetric failure modes—some suppress detections while others trigger false alarms.

Vision-language models like CLIP have become foundational components in autonomous driving perception systems, yet their robustness evaluation remains inadequate. This research addresses a critical gap by showing that traditional embedding-level stability metrics fail to capture task-aligned performance degradation. The study uses BDD100K road scenes with controlled corruptions to demonstrate that some perturbation families maintain relatively stable embeddings while catastrophically degrading hazard detection capabilities—a disconnect that current benchmarking methodologies miss entirely.

The finding that different corruption types produce asymmetric failure modes has profound implications for safety validation. While most corruptions induce false negatives (missed hazards), occlusion-based perturbations specifically trigger false alarms. This directional inconsistency suggests that uniform robustness scores obscure dangerous behavioral patterns. A model might achieve acceptable overall stability metrics while failing dangerously in specific, predictable ways—precisely the scenario that autonomous driving systems must avoid.

For the autonomous driving industry, this research challenges validation frameworks that rely solely on embedding perturbation statistics. Safety-critical systems require task-aligned stability measures that directly correlate with decision reliability rather than representation consistency. The implications extend beyond CLIP to any vision-language model deployment in safety-sensitive domains. Organizations developing or certifying autonomous systems should incorporate these findings into their robustness assessment protocols, potentially requiring more comprehensive stress-testing before deployment.

• →Embedding stability metrics fail to predict hazard detection failures in vision-language models for autonomous driving
• →Different corruption types exhibit corruption-dependent relationships between representation drift and decision instability
• →Occlusion corruptions uniquely trigger false alarms while most other perturbations suppress hazard detections
• →Current robustness benchmarks are insufficient for safety-critical autonomous driving applications
• →Task-aligned stability measures should complement embedding-level metrics in VLM safety validation