STGraphVQA: spatial-temporal graph reasoning with hierarchical cognition for interpretable driving scene understanding

Engineering notes

Experiments on DriveLM and STRIDE-QA demonstrate that STGraphVQA significantly outperforms state-of-the-art baselines, achieving a Top-1 accuracy of 76.8% and a reasoning chain completeness of 82.3%, providing a promising direction toward interpretable autonomous driving systems.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Visual question answering (VQA) in autonomous driving scenarios demands strong spatiotemporal reasoning capabilities, yet existing vision-language models lack explicit modeling of dynamic relationships in complex traffic scenes. We propose STGraphVQA, a framework that represents driving scenes as dynamic spatiotemporal graphs, where nodes denote traffic participants, edges encode spatial and semantic relationships, and the temporal dimension captures their evolution. A hierarchical reasoning architecture progressively processes information through perception, relation, and decision layers, simulating the human driving cognitive process. A logit-level constrained decoding mechanism further ensures that generated answers comply with traffic rules and physical feasibility. Experiments on DriveLM and STRIDE-QA demonstrate that STGraphVQA significantly outperforms state-of-the-art baselines, achieving a Top-1 accuracy of 76.8% and a reasoning chain completeness of 82.3%, providing a promising direction toward interpretable autonomous driving systems.

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