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

2025-11-08

AI Summary

To address the insufficient joint consideration of fairness and safety in cooperative intersection crossing for autonomous vehicles, this paper proposes a real-time hierarchical control framework. At the top layer, dynamic resource allocation is achieved via utility maximization, where fairness is formally modeled for the first time as an inequality-averse utility function incorporating historical behavior awareness. At the bottom layer, trajectory tracking is performed using Linear Quadratic Regulator (LQR), while high-order Control Barrier Functions (HOCBFs) enforce real-time safety constraints. The framework enables dynamic authority allocation among vehicles, guaranteeing zero collisions while significantly reducing average delay—thereby balancing system efficiency with near-optimal fairness. Comprehensive multi-scenario simulations validate the framework’s computational real-time performance, safety guarantees, and capability to co-optimize fairness and safety.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures

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.

Problem

• Ensuring fairness in real-time intersection control for autonomous vehicles
• Integrating inequity aversion into safety-critical traffic management systems
• Achieving collision-free coordination while maintaining efficiency and equity

Innovation

• Hierarchical control framework for intersection management
• Centralized allocation module with utility maximization
• LQR and HOCBF-based safety filter for collision avoidance