Multi-Agent Visual Reasoning for Out-of-Distribution Detection in Complex Road Environments

Out-of-Distribution (OOD) detection is critical for ensuring the reliability of semantic segmentation models in safety-critical autonomous driving scenarios. Despite recent advances, existing state-of-the-art OOD segmentation methods fundamentally rely on local features and suffer from a critical lack of contextual understanding in complex road environments. A representative example is distant scenes with small objects that require contextual reasoning to distinguish them from background elements. To evaluate such complex and challenging cases, we construct a dedicated subset for robustness assessment. The root cause stems from their inability to perform contextual semantic reasoning about object appropriateness in road environments. To address these fundamental limitations, we propose a novel multi-agent visual reasoning framework that leverages the powerful contextual understanding and semantic reasoning capabilities of Vision-Language Models (VLMs). Our framework decomposes the OOD detection task into specialized subtasks handled by multiple expert agents. This approach fundamentally shifts from local pattern recognition to in-context understanding-based OOD detection, enabling the system to understand not just what is anomalous, but why it is inappropriate for the given road context. Extensive experiments demonstrate that our framework significantly outperforms existing methods, particularly in challenging scenarios, while providing interpretable reasoning for safety-critical applications.