A Hierarchical Robust Control Strategy for Decentralized Signal-Free Intersection Management

The development of connected and automated vehicles (CAVs) is the key to improve urban mobility safety and efficiency. This paper focuses on the cooperative vehicle management at a signal-free intersection with consideration of vehicle modeling uncertainties and sensor measurement disturbances. The problem is approached by a hierarchical robust control strategy (HRCS) in a decentralized traffic coordination framework where optimal control and tube-based robust model predictive control (RMPC) methods are designed to hierarchically solve the optimal crossing order and the velocity trajectories of a group of CAVs in terms of energy consumption and throughput. To capture the energy consumption of each vehicle, their powertrain system is modeled in line with an electric drive system. With a suitable relaxation and spatial modeling approach, the optimization problems in HRCS can be formulated as convex second-order cone programs (SOCPs), which provide unique and computationally efficient solution. A rigorous proof of the equivalence between the convexified and the original problems is also provided. Simulation results illustrate the effectiveness and robustness of HRCS and reveal the impact of traffic density on the control solution. The study of the Pareto optimal solutions for the energy-time objective shows that a minor reduction in journey time can considerably reduce energy consumption, which emphasizes the necessity of optimizing their trade-off. Finally, the numerical comparisons carried out for different prediction horizons and sampling intervals provide insight into the control design.

Automated summary

This paper focuses on cooperative vehicle management at a signal-free intersection, considering vehicle modeling uncertainties and sensor measurement disturbances. A hierarchical robust control strategy is proposed, with optimal control and robust model predictive control methods designed to solve the crossing order and velocity trajectories of connected and automated vehicles. The optimization problems are formulated as convex second-order cone programs, providing computationally efficient solutions. Simulation results demonstrate the effectiveness and robustness of the proposed strategy, highlighting the trade-off between energy consumption and journey time.