Predictive Control of Connected Mixed Traffic under Random Communication Constraints
Predictive Control of Connected Mixed Traffic under Random Communication Constraints
复制标题
DOI：
10.1109/iros45743.2020.9341139
复制
发表时间：
2020-10
期刊：
影响因子：
--
通讯作者：
Longxiang Guo;Yunyi Jia
中科院分区：
文献类型：
--
作者：
Longxiang Guo;Yunyi Jia
全连接和自动化的车辆已经被设想用于帮助提高交通系统的驾驶安全性和效率。然而，在不久的将来，人类驾驶的车辆仍然会出现，这将导致连接的混合交通，而不是完全连接和自动化的交通。由于人类驾驶车辆的复杂性和连接中潜在的通信限制，这是具有挑战性的。为了解决这个问题，本文模型连接的混合流量，并提出了模型预测控制方法与各种预测方法，包括一个新的逆模型预测控制（IMPC）为基础的方法来处理随机通信延迟和数据包丢失的连接。连接混合交通的人在回路实验结果表明，所提出的方法，特别是预测控制与IMPC在处理混合交通的通信约束的有效性和优势。
Fully connected and automated vehicles have been envisioned to help improve the driving safety and efficiency of the transportation system. However, human-driven vehicles will still be present in the near future, which will lead to connected mixed traffic instead of fully connected and automated traffic. This is challenging because of the complexity of human-driving vehicles and the potential communication constraints in the connectivity. To address this issue, this paper models the connected mixed traffic and proposes model predictive control approaches with various prediction approaches including a new inverse model predictive control (IMPC) based approach to handle random communication delays and packet losses in connectivity. The human-in-the-loop experimental results for connected mixed traffic demonstrated the effectiveness and advantages of the proposed approaches, especially the predictive control with IMPC in handling communication constraints in mixed traffic.