Consensus-Aware AV Behavior: Trade-offs Between Safety, Interaction, and Performance in Mixed Urban Traffic

TL;DR

This paper investigates the challenge of achieving consensus among safety, interaction quality, and traffic performance in mixed urban traffic with automated vehicles, using empirical data analysis to quantify trade-offs.

Contribution

It introduces a framework to quantify consensus in mixed traffic, analyzing real-world data to reveal the rarity of full consensus and emphasizing the need for balanced AV models.

Findings

Full consensus across safety, interaction, and performance is rare (1.63%).

AV behavior varies significantly across different performance dimensions.

The study provides open-source tools for further research.

Abstract

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.