Intervention Strategies for Fairness and Efficiency at Autonomous Single-Intersection Traffic Flows

TL;DR

This paper explores how intervention timing affects safety, efficiency, and fairness in autonomous intersection management, proposing a MILP-based approach and analyzing early versus late intervention strategies through simulations.

Contribution

It introduces a fairness measure into MILP optimization for autonomous intersection control and studies the impact of intervention timing on system performance.

Findings

Fairness constraints can be integrated into MILP for intersection management.

Early intervention strategies improve safety and fairness.

Fairness-aware control affects platoon formation and efficiency.

Abstract

Intersections present significant challenges in traffic management, where ensuring safety and efficiency is essential for effective flow. However, these goals are often achieved at the expense of fairness, which is critical for trustworthiness and long-term sustainability. This paper investigates how the timing of centralized intervention affects the management of autonomous agents at a signal-less, orthogonal intersection, while satisfying safety constraints, evaluating efficiency, and ensuring fairness. A mixed-integer linear programming (MILP) approach is used to optimize agent coordination within a circular control zone centered at the intersection. We introduce the concept of fairness, measured via pairwise reversal counts, and incorporate fairness constraints into the MILP framework. We then study the relationship between fairness and system efficiency and its impact on platoon formation. Finally, simulation studies analyze the effectiveness of early versus late intervention strategies and fairness-aware control, focusing on safe, efficient, and robust management of agents within the control zone.