The driving interaction-a critical yet complex aspect of daily driving-lies at the core of autonomous driving research. However, real-world driving scenarios sparsely capture rich interaction events, limiting the availability of comprehensive trajectory datasets for this purpose. To address this challenge, we present InterHub, a dense interaction dataset derived by mining interaction events from extensive naturalistic driving records. We employ formal methods to describe and extract multi-agent interaction events, exposing the limitations of existing autonomous driving solutions. Additionally, we introduce a user-friendly toolkit enabling the expansion of InterHub with both public and private data. By unifying, categorizing, and analyzing diverse interaction events, InterHub facilitates cross-comparative studies and large-scale research, thereby advancing the evaluation and development of autonomous driving technologies.

翻译：驾驶交互作为日常驾驶中关键而复杂的方面，是自动驾驶研究的核心。然而，现实世界驾驶场景中丰富的交互事件捕获稀疏，限制了为此目的的综合轨迹数据集的可用性。为应对这一挑战，我们提出了InterHub——一个通过从大量自然驾驶记录中挖掘交互事件而构建的密集交互数据集。我们采用形式化方法描述和提取多智能体交互事件，揭示了现有自动驾驶解决方案的局限性。此外，我们引入了一个用户友好的工具包，支持使用公开和私有数据扩展InterHub。通过统一、分类和分析多样化的交互事件，InterHub促进了跨比较研究和大规模研究，从而推动自动驾驶技术的评估与发展。