Cooperative Platooning in Mixed Traffic of Connected, Automated, and Human-Driven Vehicles

联网、自动驾驶和人工驾驶车辆混合交通中的协作编队

• 批准号: 2009342
• 负责人: Brian Park
• 金额: $ 38.15万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2009342&HistoricalAwards=false
• 关键词: Cooperative; Platooning; Mixed; Traffic; Connected

This research project will develop cooperative platooning algorithms for mixed traffic of connected automated vehicles and conventional human-driven vehicles. Recent development of connected and automated vehicle technology allows a group of such vehicles to travel closely one after another in a safe manner (known as, cooperative platooning), which greatly improves mobility and energy efficiency. However, when a human-driven vehicle exists within the group, the cohesion of vehicle platoon is not possible, due to uncertain human driver behavior. The novel cooperative platooning algorithms to be developed in this project will enable connected and automated vehicles to safely follow human-driven vehicles at shorter headway while mitigating traffic disturbances. These algorithms will also be able to assist a human driver in connected-but-not-automated vehicle by complementing human’s imperfect behaviors. As such, the cooperative platooning can be efficiently operated at low market penetration of connected automated vehicles. This research will benefit national economic welfare and public health with improved surface transportation mobility and reduced greenhouse gas emissions. This research will enable multi-disciplinary education and collaboration in transportation engineering, cyber-physical systems, control theory, and mechanical engineering. The research team will encourage participation from diverse and underrepresented groups in the education and research. Connected automated vehicle (CAV) has been enabled to stably travel as a platoon with short headway, which leads to improvements in mobility and energy efficiency. However, it fails to work effectively in mixed traffic where CAVs are interacting with non-CAVs. The goal of this research is to develop and validate Cooperative Adaptive Cruise Control in mixed traffic (CACC-MT) that can safely and efficiently stabilize the mixed traffic including CAV, traditional vehicles and connected human-driven vehicles. CACC-MT makes the CAV capable of performing feed-forward control or model predictive control using the received information from a further preceding vehicle, when the immediately preceding vehicle is unconnected. This allows CAV to closely follow an unconnected vehicle. CACC-MT also includes a human-in-the-loop CACC algorithm that enables co-piloting the human driver based on received information from preceding connected vehicle and help the vehicle behave more smoothly and safely in the traffic turbulence. As CACC-MT adopts robust control strategies to handle uncertainties of human driver’s behavior, it ensures cooperative platooning for mixed traffic safely and efficiently without requiring any prior knowledge of the human drivers, even at the early stage of CAV deployment.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该研究项目将开发用于互联自动驾驶车辆和传统人工驾驶车辆混合交通的合作队列算法。最近发展的联网和自动化车辆技术允许一组这样的车辆以一种安全的方式一个接一个地行驶（称为合作队列），这大大提高了机动性和能源效率。然而，当群体中存在一辆人类驾驶的车辆时，由于人类驾驶行为的不确定性，车辆排的凝聚力不可能实现。在该项目中开发的新型合作队列算法将使联网和自动驾驶车辆能够以更短的车头时距安全地跟随人类驾驶的车辆，同时减轻交通干扰。这些算法还可以通过补充人类不完美的行为，帮助人类驾驶员驾驶联网但非自动的车辆。因此，在联网自动驾驶汽车市场渗透率较低的情况下，合作队列可以有效运行。该研究将通过改善地面交通机动性和减少温室气体排放，有利于国家经济福利和公众健康。这项研究将使交通工程、网络物理系统、控制理论和机械工程的多学科教育和合作成为可能。研究小组将鼓励不同和代表性不足的群体参与教育和研究。互联自动驾驶汽车（CAV）能够在短时间内稳定地排成一排行驶，从而提高了机动性和能源效率。然而，在混合交通中，当自动驾驶汽车与非自动驾驶汽车交互时，它不能有效地工作。本研究的目标是开发和验证混合交通中的协同自适应巡航控制（ccc - mt），以安全有效地稳定混合交通，包括自动驾驶汽车，传统车辆和联网的人驾驶车辆。ccc - mt使CAV能够在前一辆车未连接的情况下，利用从前一辆车接收的信息进行前馈控制或模型预测控制。这使得CAV可以密切跟踪未连接的车辆。CACC- mt还包括一种人在环路（human-in-the-loop）的CACC算法，该算法可以根据从前面连接的车辆接收到的信息，帮助人类驾驶员共同驾驶，并帮助车辆在交通动荡中更平稳、更安全。由于ccc - mt采用鲁棒控制策略来处理人类驾驶员行为的不确定性，因此即使在CAV部署的早期阶段，也无需事先了解人类驾驶员，即可确保混合交通的合作队列安全高效。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Design and Evaluation of a Human-in-the-Loop Connected Cruise Control

人在环互联巡航控制的设计和评估

• DOI: 10.1109/tvt.2022.3172507
• 发表时间: 2022
• 期刊: IEEE Transactions on Vehicular Technology
• 影响因子: 6.8
• 作者: Chen, Zheng;Park, Byungkyu Brian;Hu, Jia
• 通讯作者: Hu, Jia

Safety Assessment of Cooperative Platooning in Mixed Traffic

混合交通中协同编队的安全评估

• DOI: 10.3390/engproc2023036038
• 发表时间: 2023
• 期刊: Engineering proceedings
• 作者: Park, B. Brian;Lee, Hyejin;Yun, Ilsoo;Park, Jeehyung
• 通讯作者: Park, Jeehyung

Design and field evaluation of cooperative adaptive cruise control with unconnected vehicle in the loop

非网联车辆在环协同自适应巡航控制设计与现场评估

• DOI: 10.1016/j.trc.2021.103364
• 发表时间: 2021
• 期刊: Transportation Research Part C: Emerging Technologies
• 作者: Lee, Daegyu;Lee, Seungwook;Chen, Zheng;Park, B. Brian;Shim, David Hyunchul
• 通讯作者: Shim, David Hyunchul

Does the Intelligent Driver Model Adequately Represent Human Drivers?

智能驾驶员模型是否足以代表人类驾驶员？

• DOI: 10.5220/0000173600003479
• 发表时间: 2023
• 期刊: Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems
• 作者: Mu, Zeyu;Jahedinia, Fatemeh;Park, B. Brian
• 通讯作者: Park, B. Brian

Cooperative Platooning with Mixed Traffic on Urban Arterial Roads

城市主干道混合交通协同编队

• DOI: 10.1109/iv51971.2022.9827105
• 发表时间: 2022
• 期刊: 2022 IEEE Intelligent Vehicles Symposium (IV
• 作者: Mu, Zeyu;Chen, Zheng;Ryu, Seunghan;Avedisov, Sergei S.;Guo, Rui;Park, B. Brian
• 通讯作者: Park, B. Brian