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

TL;DR

This study investigates how sensor perturbations affect the reasoning and reliability of Vision-Language-Action models in autonomous driving, highlighting the importance of reasoning consistency as a safety indicator.

Contribution

The paper provides a comprehensive analysis of VLA robustness under sensor noise, demonstrating the value of reasoning consistency for trajectory reliability and proposing runtime monitoring strategies.

Findings

Reasoning consistency correlates strongly with trajectory reliability.

Enabling Chain-of-Causation explanations improves trajectory accuracy.

Sensor noise causes approximately linear degradation in model performance.

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 $5.3{\times}$ (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.