We focus on the problem of analyzing multiagent interactions in traffic domains. Understanding the space of behavior of real-world traffic may offer significant advantages for algorithmic design, data-driven methodologies, and benchmarking. However, the high dimensionality of the space and the stochasticity of human behavior may hinder the identification of important interaction patterns. Our key insight is that traffic environments feature significant geometric and temporal structure, leading to highly organized collective behaviors, often drawn from a small set of dominant modes. In this work, we propose a representation based on the formalism of topological braids that can summarize arbitrarily complex multiagent behavior into a compact object of dual geometric and symbolic nature, capturing critical events of interaction. This representation allows us to formally enumerate the space of outcomes in a traffic scene and characterize their complexity. We illustrate the value of the proposed representation in summarizing critical aspects of real-world traffic behavior through a case study on recent driving datasets. We show that despite the density of real-world traffic, observed behavior tends to follow highly organized patterns of low interaction. Our framework may be a valuable tool for evaluating the richness of driving datasets, but also for synthetically designing balanced training datasets or benchmarks.
翻译:我们的重点是分析交通领域的多试剂相互作用问题。了解真实世界交通行为空间可能为算法设计、数据驱动的方法和基准制定提供重大优势。然而,空间的高度维度和人类行为的随机性可能阻碍确定重要的互动模式。我们的主要见解是,交通环境具有重要的几何和时间结构,导致通常从少数主导模式中抽取的高度有组织的集体行为。在这项工作中,我们建议基于地形结构的正统主义进行表述,将任意复杂的多试剂行为归纳成一个具有双重几何和象征性质的紧凑物体,捕捉关键的相互作用事件。这种表述使我们能够正式地列出交通领域的结果空间,并描述其复杂性。我们通过对近期驱动数据集的案例研究,说明拟议在总结真实世界交通行为关键方面的代表性的价值。我们表明,尽管真实世界交通的密度很大,但观察到的行为倾向于遵循高度有组织的低互动模式。我们的框架可能是一个宝贵的工具,用以评价驱动数据集的丰富性,但也是综合设计平衡性的数据基准。