用图神经网络增强交通场景的可解释理解，提升自动驾驶决策能力。
Explainable Scene Understanding with Qualitative Representations and Graph Neural Networks
- 构建新图神经网络，整合定性表示与完整场景图结构
- 在nuScenes数据集上准确识别关键交通对象，优于基线方法
- 适合自动驾驶系统中需要透明推理的场景理解任务
本文研究将图神经网络（GNNs）与定性可解释图（QXGs）结合，用于自动驾驶中的场景理解。场景理解是后续反应或主动决策的基础，本质上是一个解释任务：为何其他交通参与者会采取某种行为？以往工作虽证明了QXGs在浅层模型中的有效性，但仅分析对象对之间的单一关系链，忽略了整体场景上下文。本文提出一种新型GNN架构，能够处理整个场景的图结构以识别相关物体。在引入DriveLM人工标注的相关性标签的nuScenes数据集上进行评估，实验表明该方法在相关物体识别任务中性能优于基线模型，有效应对类别不平衡问题，并充分考虑所有物体间的时空关系。本工作展示了将定性表示与深度学习结合在自动驾驶可解释场景理解中的潜力。
原文摘要 · Abstract (English)
This paper investigates the integration of graph neural networks (GNNs) with Qualitative Explainable Graphs (QXGs) for scene understanding in automated driving. Scene understanding is the basis for any further reactive or proactive decision-making. Scene understanding and related reasoning is inherently an explanation task: why is another traffic participant doing something, what or who caused their actions? While previous work demonstrated QXGs' effectiveness using shallow machine learning models, these approaches were limited to analysing single relation chains between object pairs, disregarding the broader scene context. We propose a novel GNN architecture that processes entire graph structures to identify relevant objects in traffic scenes. We evaluate our method on the nuScenes dataset enriched with DriveLM's human-annotated relevance labels. Experimental results show that our GNN-based approach achieves superior performance compared to baseline methods. The model effectively handles the inherent class imbalance in relevant object identification tasks while considering the complete spatial-temporal relationships between all objects in the scene. Our work demonstrates the potential of combining qualitative representations with deep learning approaches for explainable scene understanding in autonomous driving systems.
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