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.

翻译：在交叉路口协调联网自动驾驶车辆时确保公平性，对于实现公平通行、社会接受度以及长期系统效率至关重要，然而在安全关键、实时的交通控制中，这一问题仍未得到充分探索。本文提出了一种公平感知的分层控制框架，明确地将不平等厌恶整合到交叉路口管理中。在顶层，一个集中式分配模块通过最大化一个考虑等待时间、紧急性、控制历史以及速度偏差的效用函数，来分配控制权限（即选择单一车辆执行其轨迹）。在底层，被授权的车辆使用线性二次调节器（LQR）执行预计算的轨迹，并应用基于高阶控制屏障函数（HOCBF）的安全过滤器进行实时避撞。在不同交通需求和需求分布下的仿真结果表明，所提出的框架实现了近乎完美的公平性，消除了碰撞，降低了平均延误，并保持了实时可行性。这些结果突出表明，公平性可以在不牺牲安全性或性能的情况下被系统地纳入，从而为未来的自主交通系统实现可扩展且公平的协调。