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

arXiv: https://arxiv.org/abs/2505.04379 · HTML: https://mlsanity.com/paper/2505.04379/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 7 May 2025.

## Builds on (most cited first, 6 of 6)

- [SAFR-AV: Safety Analysis of Autonomous Vehicles using Real World Data -- An end-to-end solution for real world data driven scenario-based testing for pre-certification of AV stacks](https://mlsanity.com/paper/2302.14601/) (2023)
- [Pedestrian and Passenger Interaction with Autonomous Vehicles: Field Study in a Crosswalk Scenario](https://mlsanity.com/paper/2312.07606/) (2023)
- [V2V-based Collision-avoidance Decision Strategy for Autonomous Vehicles Interacting with Fully Occluded Pedestrians at Midblock on Multilane Roadways](https://mlsanity.com/paper/2303.13032/) (2023)
- [Consensus controller with safety guarantee: an application to the kinematic bicycle model](https://mlsanity.com/paper/2303.17584/) (2023)
- [Distributed Model Predictive Control for Heterogeneous Platoons with Affine Spacing Policies and Arbitrary Communication Topologies](https://mlsanity.com/paper/2404.12441/) (2024)
- [A Data-Driven Analysis of Vulnerable Road User Safety in Interaction with Connected Automated Vehicles](https://mlsanity.com/paper/2404.14935/) (2024)

## Abstract (arXiv)

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.