明智选择博弈：测量现实世界车辆交互中的博弈论结构
Choose Your Game Wisely: Measuring Game-Theoretic Structures in Real-World Vehicle Interactions
浏览论文内容
中文总结 AI 辅助
本文开发了基于轨迹的交互测量框架，在六个现实世界车辆轨迹数据集上验证，发现现实车辆交互有并发、顺序等结构，不同博弈论模型是互补而非通用的建模抽象。
中文摘要 AI 辅助
博弈论模型为车辆交互建模提供了原则性框架，但其潜在的时间假设尚未针对现实世界驾驶行为进行系统检验。尤其，仍不清楚如何从车辆轨迹中测量同时、顺序及非对称的交互结构。本文开发了一种基于轨迹的交互测量框架，用于识别交互事件并量化行为变化起始、时间组织、起始后响应动态及排序稳定性。该框架利用行为偏差验证候选交互。我们在六个现实世界轨迹数据集上评估该框架，包括INTERACTION、highD、inD、rounD、Waymo Open Motion和nuPlan，涵盖多样的道路几何、交通环境及交互类型。结果显示，并发和顺序行为变化在观测到的跟驰、汇入及冲突交互中均占相当比例。在顺序交互中，稳定排序比交替排序更普遍，表明持续非对称角色是常见的交互结构。重要的是，时间优先级不一定与可测量的行为响应重合，说明仅时间排序可能不足以表征行为依赖。这些发现表明，现实世界交互呈现并发、顺序及持续有序的时间结构。因此，不同的博弈论公式应被视为针对不同交互 regime 的互补建模抽象，而非支配所有车辆交互的通用结构。
英文摘要
Game-theoretic models provide principled frameworks for modeling vehicle interactions, but their underlying temporal assumptions have not been systematically examined against real-world driving behavior. In particular, it remains unclear how simultaneous, sequential, and asymmetric interaction structures can be measured from vehicle trajectories. This paper develops a trajectory-based interaction measurement framework to identify interaction events and quantify behavioral change onset, temporal organization, post-onset response dynamics, and ordering stability. The framework uses behavioral deviations to verify candidate interactions. We evaluate the framework on six real-world trajectory datasets, including INTERACTION, highD, inD, rounD, Waymo Open Motion, and nuPlan, covering diverse road geometries, traffic environments, and interaction types. The results show that concurrent and sequential behavioral changes both constitute substantial proportions of observed following, merging, and conflicting interactions. Among sequential interactions, stable ordering is more prevalent than alternating ordering, indicating that persistent asymmetric roles are a common interaction structure. Importantly, temporal precedence does not necessarily coincide with a measurable behavioral response, indicating that temporal ordering alone may not be sufficient to characterize behavioral dependence. These findings show that real-world interactions exhibit concurrent, sequential, and persistently ordered temporal structures. Different game-theoretic formulations are therefore better regarded as complementary modeling abstractions for different interaction regimes rather than as a universal structure governing all vehicle interactions.
发表机构
- School of Automation and Intelligent Sensing, Shanghai Jiao Tong University（上海交通大学自动化与智能感知学院）
- School of Airspace Science and Engineering, Shandong University（山东大学空天科学与工程学院）
- Global Institute of Future Technology, Shanghai Jiao Tong University（上海交通大学未来技术全球研究院）
- Global College, Shanghai Jiao Tong University（上海交通大学全球学院）
机构由 AI 辅助整理，请以论文原文为准。
