构建大规模驾驶交互数据集，支持自动驾驶多智能体行为分析与语言指令训练。
An interactive enhanced driving dataset for autonomous driving
- 从五个自然驾驶数据集构建交互导向的大型数据集，覆盖多种典型交通场景。
- 包含731万段交互片段，666万个多智能体交互案例，每段配有交互强度与效率指标。
- 提供重构的俯视视频、结构化语义和多轮问答，适用于复杂交互建模与评估。
驾驶交互数据对训练和评估自动驾驶视觉-语言-动作（VLA）模型至关重要，但现有数据集交互样本稀疏且轨迹、视觉输入与语言标注对齐较弱。本文提出交互增强型驾驶数据集（IEDD），基于五项自然驾驶轨迹数据集（Lyft Level 5、Waymo、nuPlan、INTERACTION、SIND）构建，包含731万条以车辆为中心的交互片段，其中666万条涉及多智能体交互，涵盖对向、跟车、变道和交叉等典型场景。每段均配有基于轨迹的交互强度与效率度量。在此基础上，IEDD-VQA进一步提供轨迹重建的俯视图视频、结构化交互语义及多轮问答对。该数据集可支持交互挖掘、长尾场景分析、VLA指令微调以及感知、行为描述、物理量化与反事实推理的分层评估。
原文摘要 · Abstract (English)
Driving interaction data are important for training and evaluating autonomous drivingVision-Language-Action (VLA) models, but existing datasets contain limited denseinteraction samples and weak alignment between trajectories, visual inputs, and languageannotations. This work presents the Interactive Enhanced Driving Dataset (IEDD), alarge-scale interaction-oriented dataset constructed from five naturalistic trajectory datasets:Lyft Level 5, Waymo, nuPlan, INTERACTION, and SIND. IEDD contains 7.31 millionego-centric interaction segments, including 6.66 million multi agents cases, covering head-on,car-following, merging, and crossing interactions. Each segment is associated withtrajectory-derived interaction metrics describing interaction intensity and efficiency. Based onthese annotations, IEDD-VQA further provides trajectory-reconstructed BEV videos,structured interaction semantics, and multi-turn question-answer pairs. The dataset cansupport interaction mining, long-tail scenario analysis, VLA instruction tuning, andhierarchical evaluation of perception, behavior description, physical quantification, andcounterfactual reasoning.
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