Lost in Fog: Sensor Perturbations Expose Reasoning Fragility in Driving VLAs

arXiv:2605.21446v1 Announce Type: new Abstract: Interpretable autonomous driving planners depend not only on generating explanations, but also on those explanations remaining reliable under real-world sensor degradation. In this paper we present a controlled perturbation study of Vision-Language-Action (VLA) robustness in autonomous driving, evaluating Alpamayo R1 (10B parameters) across 1,996 scenarios under eight sensor perturbations (Gaussian noise at four intensities, two lighting extremes, and two fog levels; {sim}18{,}000 inference trials). We find that reasoning consistency is a high-fidelity indicator of trajectory reliability: when Chain-of-Causation (CoC) explanations change after perturbation, trajectory deviation spikes 5.3{imes} (21.8m vs 4.1m), with r!=!0.99 across attack types and r_{pb}!=!0.53 per-sample (Cohen's d!=!1.12). A controlled ablation provides evidence that enabling CoC generation is associated with improved trajectory accuracy (11.8% on average across conditions; p < 0.0001) under matched inference settings. Over the tested noise range (sigma in {10, 30, 50, 70}), degradation is approximately linear (R^2!=!0.957), while standard input preprocessing defenses provide only marginal relief. Together, these results establish CoC consistency as a quantitative proxy for planning safety and motivate reasoning-based runtime monitoring for safer VLA deployment.

Source: arXiv cs.RO | 2026-05-21