Decentralized Optimal Merging Control for Connected and Automated Vehicles with Optimal Dynamic Resequencing
Decentralized Optimal Merging Control for Connected and Automated Vehicles with Optimal Dynamic Resequencing
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DOI：
10.23919/acc45564.2020.9147805
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Wei Xiao;C. Cassandras
中科院分区：
文献类型：
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作者：
Wei Xiao;C. Cassandras
在早期的工作中，基于在给定的控制区（CZ）上的先进先出（FIFO）假设，提供了一个完整的解决方案，以分散的最优合并问题的连接和自动驾驶车辆（CAV）。在本文中，我们放松FIFO的假设，并提出了一个分散的最优动态重排序（ODR）算法，以进一步提高性能的所有合并CAV的时间和能量。具体来说，我们引入了一个重排序区（RZ）之前的CZ内的每一个CAV可以执行ODR算法。我们确定了触发ODR的最晚可能时间，以便最小限度地影响CAV的操作。仿真结果表明，在CAV旅行时间和能源消耗的合并和主要道路，ODR的好处，优于早期的结果在不同的重排序方案。
A complete solution to a decentralized optimal merging problem for Connected and Automated Vehicles (CAVs) was provided in earlier work, based on a First In First Out (FIFO) assumption over a given Control Zone (CZ). In this paper, we relax the FIFO assumption and propose a decentralized Optimal Dynamic Resequencing (ODR) algorithm to further improve the performance of all merging CAVs in terms of time and energy. Specifically, we introduce a Resequencing Zone (RZ) prior to the CZ within which every CAV can execute the ODR algorithm. We determine the latest possible time for triggering ODR so as to minimally affect the CAV’s operation. Simulation results show significant ODR benefits in CAV travel times and energy consumption over the merging and main roads, outperforming earlier results under different resequencing schemes.