论文标题
数据驱动的连接和自动化车辆的分布式交叉管理
Data-Driven Distributed Intersection Management for Connected and Automated Vehicles
论文作者
论文摘要
这项工作解决了连接和自动化车辆的孤立交叉路口的自动交通管理问题。我们将每个车辆的轨迹分解为两个阶段：临时阶段和协调阶段。进入感兴趣区域后，车辆最初是在临时阶段运行的，在此期间，车辆优化其轨迹，但受到进入交叉路口的约束。定期，临时阶段中的所有车辆都转移到其协调阶段，这是通过对车辆交叉点使用序列及其轨迹的序列的协调优化而获得的。对于协调阶段，我们提出了一个数据驱动的解决方案，其中通过数据驱动的在线分类获得了相交的用法序列，并顺序计算轨迹。这种方法还允许将宏观信息（例如流量到达率）纳入解决方案。总体算法证明是安全的，可以以分布式的方式实施。最后，我们将所提出的算法与传统的交叉管理方法以及通过模拟进行了一些现有文献进行了比较。通过模拟，我们还证明了在广泛的流量到达速率上，每辆车的计算时间保持恒定。
This work addresses the problem of autonomous traffic management at an isolated intersection for connected and automated vehicles. We decompose the trajectory of each vehicle into two phases: the provisional phase and the coordinated phase. A vehicle, upon entering the region of interest, initially operates in the provisional phase, during which the vehicle optimizes its trajectory but is constrained from entering the intersection. Periodically, all the vehicles in their provisional phase switch to their coordinated phase, which is obtained by coordinated optimization of the sequence of the vehicles' intersection usage as well as their trajectories. For the coordinated phase, we propose a data driven solution, in which the intersection usage sequence is obtained through a data-driven online classification and the trajectories are computed sequentially. This approach also allows for the incorporation of macro information such as traffic arrival rates into the solution. The overall algorithm is provably safe and can be implemented in a distributed manner. Finally, we compare the proposed algorithm against traditional methods of intersection management and against some existing literature through simulations. Through simulations, we also demonstrate that the computation time per vehicle remains constant for the proposed algorithm over a wide range of traffic arrival rates.
