发表机构
University of Nebraska–Lincoln（内布拉斯加大学林肯分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出量子启发式驾驶行为表示，在I-24 MOTION数据集无监督训练后恢复出三种可解释驾驶模式，可支持跟驰模型与自动驾驶车辆的实际应用，且开源了相关工具包。
AI 中文摘要
驾驶行为具有异质性、依赖上下文且随时间变化，这些特性塑造了我们观测到的交通现象。然而，大多数模型会预先确定哪些行为变量相互作用以及如何作用，超出该形式的行为会被当作噪声吸收，而足够灵活以捕捉这类行为的模型往往会丧失可解释性。我们提出一种驾驶行为的量子启发式表示，它结合了通常被单独或部分处理的特性：它是连续的、概率性的、依赖上下文的、依赖历史的，并将行为变量之间的交互表示为从数据中学习得到的结果。每位驾驶员被编码为一个演化的密度矩阵，为行为不确定性、时间演化以及依赖上下文的行为变化提供了统一的表示。在I-24 MOTION数据集上进行无监督训练后，该框架恢复出三种可解释的驾驶模式，对应三种状态：自由流、过渡状态和拥堵状态。这些模式捕捉了数据中的行为范围，以及驾驶员在条件变化时在各状态间的平滑过渡。该表示还能重现已知的宏观现象，与基本图一致并重现滞后环。我们还展示了该表示如何支持实际应用：它为经典的跟驰模型提供依赖上下文的参数，并为自动驾驶车辆提供周围驾驶员的实时行为解读，以及对其运动的短程预测。该框架指明了构建可解释且可信的交通模型的方向。我们在GitHub上发布了一个开源工具包，涵盖数据处理、训练、推理和分析。
英文摘要
Driver behavior is heterogeneous, context-dependent, and changes over time, and these properties shape the traffic phenomena we observe. Most models, however, fix in advance which behavioral variables interact and how. Behavior outside that form is absorbed as noise, while models flexible enough to capture it tend to lose interpretability. We introduce a quantum-inspired representation of driver behavior that combines properties usually treated separately or in part: it is continuous, probabilistic, context-dependent, history-dependent, and represents interactions among behavioral variables as learned from data. Each driver is encoded as an evolving density matrix, providing a unified representation of behavioral uncertainty, temporal evolution, and context-dependent behavioral variation. Trained without supervision on the I-24 MOTION dataset, the framework recovers three interpretable driving profiles representing three regimes: free flow, transition, and congestion. The profiles capture the behavioral range of the data and the smooth transitions drivers make between regimes as conditions change. The same representation also reproduces known macroscopic phenomena, aligning with the fundamental diagram and reproducing hysteresis loops. We also show how the representation supports practical use: it supplies context-dependent parameters to classical car-following models, and gives an autonomous vehicle a live behavioral read of the surrounding drivers with a short-horizon forecast of their motion. The framework points toward models of traffic that are interpretable and trustworthy by construction. We release an open-source toolkit on GitHub (https://github.com/mselayan/quantum-driver-representation) spanning data processing, training, inference, and analysis.
Comments44 pages (including Appendices), 27 figures. Submitted to Transportation Research Part B: Methodological. Code and toolkit: https://github.com/mselayan/quantum-driver-representation
