构建了密集交互轨迹数据集InterHub，助力自动驾驶研究
InterHub: A Naturalistic Trajectory Dataset with Dense Interaction for Autonomous Driving
- 从自然驾驶数据中挖掘多智能体交互事件，构建密集交互数据集
- 涵盖多样化交互场景，支持自动驾驶系统评估与对比研究
- 提供易用工具包，可扩展公共与私有数据，适合算法研发者
驾驶交互——日常驾驶中关键但复杂的环节——是自动驾驶研究的核心。然而，真实世界驾驶场景中稀疏地捕捉到丰富的交互事件，限制了全面轨迹数据集的可用性。为解决这一挑战，我们提出InterHub，一个通过挖掘大量自然驾驶记录中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并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，我们引入一个用户友好的工具包，支持以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科研，从而推进自动驾驶技术的评估与开发。
原文摘要 · Abstract (English)
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.
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