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.

本研究解决了在复杂的交通系统中自动驾驶车辆（AV）与人类驾驶车辆（HDV）之间实现安全性、互动质量和交通性能共识的挑战。通过分析高分辨率的轨迹数据，研究发现仅1.63%的AV与脆弱道路用户（VRU）的交互满足安全、互动和性能的全面共识，强调了在混合交通环境中开发自动驾驶模型以平衡多维性能的重要性。

共识意识的自动驾驶行为：在混合城市交通中的安全性、互动性和性能之间的权衡