SignReasoner: Compositional Reasoning for Complex Traffic Sign Understanding via Functional Structure Units

TL;DR

SignReasoner introduces a compositional reasoning framework using Functional Structure Units to improve complex traffic sign understanding in vision-language models, enabling better generalization and data efficiency.

Contribution

It proposes a novel FSU-based decomposition method and a two-stage post-training pipeline to enhance VLMs' traffic sign reasoning without changing their architecture.

Findings

SignReasoner achieves state-of-the-art results on TrafficSignEval.

The method improves generalization to unseen sign configurations.

It enhances reasoning accuracy with high data efficiency.

Abstract

Accurate semantic understanding of complex traffic signs-including those with intricate layouts, multi-lingual text, and composite symbols-is critical for autonomous driving safety. Current models, both specialized small ones and large Vision Language Models (VLMs), suffer from a significant bottleneck: a lack of compositional generalization, leading to failure when encountering novel sign configurations. To overcome this, we propose SignReasoner, a novel paradigm that transforms general VLMs into expert traffic sign reasoners. Our core innovation is Functional Structure Unit (FSU), which shifts from common instance-based modeling to flexible function-based decomposition. By breaking down complex signs into minimal, core functional blocks (e.g., Direction, Notice, Lane), our model learns the underlying structural grammar, enabling robust generalization to unseen compositions. We define this decomposition as the FSU-Reasoning task and introduce a two-stage VLM post-training pipeline to maximize performance: Iterative Caption-FSU Distillation that enhances the model's accuracy in both FSU-reasoning and caption generation; FSU-GRPO that uses Tree Edit Distance (TED) to compute FSU differences as the rewards in GRPO algorithm, boosting reasoning abilities. Experiments on the newly proposed FSU-Reasoning benchmark, TrafficSignEval, show that SignReasoner achieves new SOTA with remarkable data efficiency and no architectural modification, significantly improving the traffic sign understanding in various VLMs.