论文标题
连接和自动化车辆的最佳时间轨迹和协调
Optimal Time Trajectory and Coordination for Connected and Automated Vehicles
论文作者
论文摘要
在本文中，我们提供了一个分散的理论框架，用于在不同的交通情况下协调连接和自动化车辆（CAVS）。该框架包括：（1）每个骑士的高级优化，其最佳的时间轨迹和车道可以通过给定的交通情况，同时减轻交通拥堵； （2）一个低级优化，可为每个CAV产生其最佳控制输入（加速/减速）。我们提供了包括后端，依赖速度的安全性约束的低级优化问题的完整分析解决方案。此外，我们为没有二元性差距的高级优化提供了问题制定。后者意味着每个CAV的最佳时间轨迹不会激活低级优化的任何状态，控制和安全约束，从而允许在线实施。最后，我们提出了一个具有超平面的几何双重性框架，以得出始终存在上层优化的最佳解决方案的条件。我们通过模拟验证了提出的理论框架的有效性。
In this paper, we provide a decentralized theoretical framework for coordination of connected and automated vehicles (CAVs) at different traffic scenarios. The framework includes: (1) an upper-level optimization that yields for each CAV its optimal time trajectory and lane to pass through a given traffic scenario while alleviating congestion; and (2) a low-level optimization that yields for each CAV its optimal control input (acceleration/deceleration). We provide a complete, analytical solution of the low-level optimization problem that includes the rear-end, speed-dependent safety constraint. Furthermore, we provide a problem formulation for the upper-level optimization in which there is no duality gap. The latter implies that the optimal time trajectory for each CAV does not activate any of the state, control, and safety constraints of the low-level optimization, thus allowing for online implementation. Finally, we present a geometric duality framework with hyperplanes to derive the condition under which the optimal solution of the upper-level optimization always exists. We validate the effectiveness of the proposed theoretical framework through simulation.
