Vision-Language Models (VLMs) have recently shown remarkable progress in multimodal reasoning, yet their applications in autonomous driving remain limited. In particular, the ability to understand road topology, a key requirement for safe navigation, has received relatively little attention. While some recent works have begun to explore VLMs in driving contexts, their performance on topology reasoning is far from satisfactory. In this work, we systematically evaluate VLMs' capabilities in road topology understanding. Specifically, multi-view images are projected into unified ground-plane coordinate system and fused into bird's-eye-view (BEV) lanes. Based on these BEV lanes, we formulate four topology-related diagnostic VQA tasks, which together capture essential components of spatial topology reasoning. Through extensive evaluation, we find that while frontier closed-source models (e.g., GPT-4o) achieve relatively high accuracy in some tasks, they still fail in some temporal questions that humans can answer (e.g., GPT-4o achieve only 67.8% in vector, a two-class classification problem). Furthermore, we find open-source VLMs, even at 30B scale, struggle significantly. These results indicate that spatial reasoning remains a fundamental bottleneck for current VLMs. We also find that the model's capability is positively correlated with model size, length of reasoning tokens and shots provided as examples, showing direction for future research.
Chen et al. (Sat,) studied this question.