Abstract
Vision-Language Models (VLMs) are increasingly deployed in autonomous driving and embodied AI systems, where reliable perception is critical for safe semantic reasoning and decision-making. While recent VLMs demonstrate strong performance on multimodal benchmarks, their robustness to realistic perception degradation remains poorly understood. In this work, we systematically study semantic misalignment in VLMs under controlled degradation of upstream visual perception, using semantic segmentation on the Cityscapes dataset as a representative perception module. We introduce perception-realistic corruptions that induce only moderate drops in conventional segmentation metrics, yet observe severe failures in downstream VLM behavior, including hallucinated object mentions, omission of safety-critical entities, and inconsistent safety judgments. To quantify these effects, we propose a set of language-level misalignment metrics that capture hallucination, critical omission, and safety misinterpretation, and analyze their relationship with segmentation quality across multiple contrastive and generative VLMs. Our results reveal a clear disconnect between pixel-level robustness and multimodal semantic reliability, highlighting a critical limitation of current VLM-based systems and motivating the need for evaluation frameworks that explicitly account for perception uncertainty in safety-critical applications.
Abstract (translated)
视觉-语言模型（VLM）在自动驾驶和具身AI系统中越来越广泛地被部署，而在这些领域中可靠的感知对于安全的语义推理和决策至关重要。尽管最近的VLM在多模态基准测试中表现出强大的性能，但它们对现实世界中感知退化情况下的鲁棒性仍然知之甚少。在这项工作中，我们系统地研究了在上游视觉感知受控退化的条件下VLM中的语义错配问题，使用Cityscapes数据集上的语义分割作为典型的感知模块。我们引入了一组逼真的感知退化形式，这些退化仅导致传统分割指标有轻微下降，但观察到下游VLM行为出现严重故障，包括虚构对象提及、关键实体遗漏以及不一致的安全判断。为了量化这些效应，我们提出了一系列语言层面的错配度量标准，捕捉虚幻生成、关键信息遗漏和安全误解，并分析了它们与多个对比和生成型VLM之间分割质量的关系。我们的结果揭示了一个清晰的事实：像素级鲁棒性和多模态语义可靠性之间的明显脱节，突显出当前基于VLM系统的重大限制，并强调在安全性至关重要的应用中需要明确考虑感知不确定性评估框架的必要性。
URL
https://arxiv.org/abs/2601.08355
