研究自动驾驶在城市交通中如何平衡安全、交互与效率的冲突。
Consensus-Aware AV Behavior: Trade-offs Between Safety, Interaction, and Performance in Mixed Urban Traffic
- 以真实轨迹数据量化自动驾驶与人类车辆在交叉口的行为共识
- 仅1.63%的交互场景同时满足安全、交互和性能三重标准
- 为混合交通中自动驾驶系统设计提供多目标权衡依据
交通系统长期受复杂性与异质性驱动，源于行为主体行为与交通结果之间的相互依赖。自动驾驶车辆（AV）的部署引入新挑战：在安全、交互质量与交通性能之间达成共识。本文将共识视为交通系统的根本属性，并尝试量化其程度。基于第三代仿真数据集（TGSIM）的高分辨率轨迹数据，我们实证分析了自动驾驶车辆与人类驾驶车辆（HDV）在信号控制城市路口及弱势道路使用者（VRU）周围的交互行为。评估了时间到碰撞（TTC）、碰撞后时间（PET）、减速度模式、车距及串稳定性等关键指标，在安全、交互质量和交通性能三个维度上进行分析。结果显示，三者完全一致的情况极为罕见，仅有1.63%的AV-VRU交互帧同时满足所有三个条件。该发现表明，需开发能显式权衡多维性能的自动驾驶模型。完整可复现性通过开源代码库（https://github.com/wissamkontar/Consensus-AV-Analysis）支持。
原文摘要 · Abstract (English)
Transportation systems have long been shaped by complexity and heterogeneity, driven by the interdependency of agent actions and traffic outcomes. The deployment of automated vehicles (AVs) in such systems introduces a new challenge: achieving consensus across safety, interaction quality, and traffic performance. In this work, we position consensus as a fundamental property of the traffic system and aim to quantify it. We use high-resolution trajectory data from the Third Generation Simulation (TGSIM) dataset to empirically analyze AV and human-driven vehicle (HDV) behavior at a signalized urban intersection and around vulnerable road users (VRUs). Key metrics, including Time-to-Collision (TTC), Post-Encroachment Time (PET), deceleration patterns, headways, and string stability, are evaluated across the three performance dimensions. Results show that full consensus across safety, interaction, and performance is rare, with only 1.63% of AV-VRU interaction frames meeting all three conditions. These findings highlight the need for AV models that explicitly balance multi-dimensional performance in mixed-traffic environments. Full reproducibility is supported via our open-source codebase on https://github.com/wissamkontar/Consensus-AV-Analysis.
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