- The paper's main contribution is a hierarchical robust strategy that integrates optimal control with tube-based RMPC for decentralized intersection management.
- It transforms nonlinear vehicle dynamics into a convex optimization framework to efficiently schedule crossing orders and reduce energy consumption.
- Simulation results demonstrate improved energy-travel time trade-offs compared to FIFO and nominal MPC methods, highlighting practical urban mobility benefits.
A Hierarchical Robust Control Strategy for Decentralized Signal-Free Intersection Management
Abstract
The paper "A Hierarchical Robust Control Strategy for Decentralized Signal-Free Intersection Management" (2206.14986) proposes a novel control strategy designed to enhance the operation of connected and automated vehicles (CAVs) as they navigate signal-free intersections. This strategy is aimed at optimizing vehicle throughput and minimizing energy consumption, addressing vehicle modeling uncertainties and disturbances from sensor measurements. A decentralized hierarchical robust control approach is adopted, utilizing optimal control and tube-based robust model predictive control (RMPC) methods to systematically address the coordination of CAVs.
Introduction
Urban traffic management systems face increasing challenges from congestion, energy consumption, and traffic accidents. The advent of CAVs offers potential solutions by leveraging vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications to improve traffic efficiency and safety. While previous studies have extensively explored centralized intersection management schemes, decentralized approaches are increasingly relevant due to their lower computational demands and resilience against central controller failures.
The paper focuses on a decentralized model-based approach for signal-free intersections, optimizing crossing orders and vehicle trajectories to reduce energy usage and travel time. Vehicle powertrain models, specifically electric drive systems, are incorporated into the control strategy to reflect realistic energy consumption dynamics.
Hierarchical Robust Control Strategy
System Architecture and Problem Setup
The paper describes a signal-free intersection setup where CAVs navigate from a control zone (CZ) to a merging zone (MZ). The objective is to minimize both travel time and energy consumption while ensuring safety through collision avoidance constraints. An optimal control problem (OCP) is formulated in the spatial domain to avoid time-domain free-end issues and to leverage convex optimization frameworks.
Convex Modeling Approach
A key contribution of the paper is the convex formulation of the control problem, transforming nonlinear dynamics into a convex optimization problem. This involves relaxing constraints and defining an auxiliary control variable to address non-affine dynamics. The equivalence between the convexified and original problems is rigorously established, ensuring that the convex solution retains the properties of the original problem.
Hierarchical Control Framework
The control strategy is divided into two hierarchical levels: an upper-level crossing order scheduler and a lower-level RMPC for trajectory optimization. The upper-level scheduler determines optimal crossing sequences based on ideal entry/exit timestamps for vehicles, using heuristic rules to balance throughput and travel time. The lower-level RMPC exploits tube-based invariant sets to account for disturbances, enabling robust, real-time trajectory planning.
Numerical Results and Discussion
Simulation results demonstrate the robustness and effectiveness of the hierarchical control strategy, with significant improvements in energy-time trade-offs compared to traditional FIFO and nominal MPC-based strategies. The results show that the hierarchical approach provides substantial energy reductions for slight compromises in travel time, indicating the importance of optimizing this trade-off in intersection management.
Simulations also reveal the influence of traffic density and prediction horizon lengths on control outcomes, highlighting the optimality compromises at higher traffic densities. The computational efficiency of the approach suggests feasibility for practical implementation, with manageable computational loads even with large numbers of CAVs.
Conclusion
This study presents a robust and decentralized framework for managing CAVs at signal-free intersections, offering computational efficiency and resilience against disturbances. The hierarchical approach successfully integrates optimal control and RMPC to optimize crossing orders and vehicle trajectories. The paper's findings underscore the potential of decentralized strategies in urban traffic management systems, paving the way for future research into vehicle coordination in complex, unsignalized environments.
Simulation trials validate the framework's robustness and effectiveness, with considerable gains in energy efficiency reflected in the Pareto front analysis. The approach promises substantial benefits for urban mobility, demonstrating the value in harmonizing throughput and energy consumption optimization in autonomous traffic systems.
