AI 中文总结
研究无车道高速公路上CAV控制问题，针对其非线性动力学和安全约束挑战，采用分散MPC算法，开发解耦方法转化约束，仿真表明算法实现安全高效轨迹且提升可扩展性，指出最佳控制策略取决于多方面权衡。
AI 中文摘要
在无车道高速公路上运行的联网自动驾驶车辆（CAV）可大幅提高交通效率。但其固有的非线性动力学以及耦合的非凸安全约束给控制设计带来了严峻挑战。集中式模型预测控制（MPC）虽能确保安全，但存在可扩展性和通信限制。本文研究用于CAV协调的分散MPC（DMPC），聚焦于迭代、非合作算法，包括雅可比型和高斯 - 赛德尔型。受缓冲Voronoi单元启发，开发了一种新颖的解耦方法将非凸安全约束转化为凸的、局部可执行的约束。仿真结果表明，所提出的DMPC算法实现了安全高效的车辆轨迹，同时显著提高了可扩展性，突出了其在未来无车道CAV交通系统中的潜力。最终结果表明，最合适的分散控制策略取决于在安全、性能和计算效率之间所需的权衡。
英文摘要
Connected and Automated Vehicles (CAVs) operating on lane-free highways offer substantial gains in traffic efficiency. However, their inherent nonlinear dynamics and the presence of coupled, nonconvex safety constraints present critical challenges to control design. Centralized Model Predictive Control (MPC) ensures safety, but suffers from scalability and communication limitations. To address these challenges, this paper investigates decentralized MPC (DMPC) for CAV coordination, focusing on iterative, non-cooperative algorithms, including Jacobi-type and Gauss-Seidel-type. A novel decoupling method is developed to transform nonconvex safety constraints into convex, locally enforceable constraints, inspired by buffered Voronoi cells. The simulation results show that the proposed DMPC algorithms achieve safe and efficient vehicle trajectories while substantially improving scalability, highlighting their potential for future lane-free CAV traffic systems. Ultimately, the results indicate that the most suitable decentralized control strategy depends on the desired trade-off between safety, performance, and computational efficiency.
Comments11 pages, 4 figures, Conference paper, Accepted for presentation at hEART 2026
