Fair and Safe: A Real-Time Hierarchical Control Framework for Intersections

TL;DR

This paper introduces a hierarchical control framework for intersection management that ensures fairness, safety, and efficiency in real-time autonomous vehicle coordination, using a utility-based allocation and safety filtering.

Contribution

It presents a novel fairness-aware hierarchical control approach that explicitly incorporates inequity aversion into intersection traffic management.

Findings

Achieves near-perfect fairness in vehicle coordination.

Eliminates collisions in simulated scenarios.

Reduces average vehicle delay while maintaining real-time operation.

Abstract

Ensuring fairness in the coordination of connected and automated vehicles at intersections is essential for equitable access, social acceptance, and long-term system efficiency, yet it remains underexplored in safety-critical, real-time traffic control. This paper proposes a fairness-aware hierarchical control framework that explicitly integrates inequity aversion into intersection management. At the top layer, a centralized allocation module assigns control authority (i.e., selects a single vehicle to execute its trajectory) by maximizing a utility that accounts for waiting time, urgency, control history, and velocity deviation. At the bottom layer, the authorized vehicle executes a precomputed trajectory using a Linear Quadratic Regulator (LQR) and applies a high-order Control Barrier Function (HOCBF)-based safety filter for real-time collision avoidance. Simulation results across varying traffic demands and demand distributions demonstrate that the proposed framework achieves near-perfect fairness, eliminates collisions, reduces average delay, and maintains real-time feasibility. These results highlight that fairness can be systematically incorporated without sacrificing safety or performance, enabling scalable and equitable coordination for future autonomous traffic systems.