Collaborative Research: Mixed Traffic Dynamics Under Disturbances: Impact of Multi-Class Connected and Automated Vehicles

合作研究：干扰下的混合交通动态：多类互联和自动驾驶车辆的影响

• 批准号: 1932921
• 负责人: Danjue Chen
• 金额: $ 9.99万
• 依托单位: University of Massachusetts Lowell
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-11-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1932921&HistoricalAwards=false
• 关键词: Collaborative; Research; Mixed; Traffic; Dynamics

Connected and Automated Vehicle (CAV) technologies have garnered huge interest across private industry, academia, government, and the public. A wide range of benefits are predicted when these ground-breaking technologies become mature, including higher road efficiency, improved safety, and better energy consumption and emissions. However, these benefits will be open to question until the technologies sufficiently mature. Specifically, a major uncertainty in benefits lies in mixed traffic of CAVs and human-driven vehicles (HDVs), where interactions between them remain largely unknown. Therefore, in the foreseeable future, traffic will likely be mixed with multiple classes of CAVs and HDVs. This project will aim to better understand the anticipated behavior of this mixed traffic system, and its impact on traffic in order to help fully utilize the potentials of the CAV technology. The results will guide the development of traffic management strategies, policies, and long-term planning for the future transportation system. This project will also engage in a range of integrated research, educational and outreach activities that will extend the knowledge obtained from this research to a broader audience, including developing simulation-based educational modules, organizing workshops, sharing simulation platform for mixed traffic, and engaging undergraduate and graduate students, particularly underrepresented groups, in the research and education.This research aims to understand how HDVs and different classes of CAVs will interact under traffic disturbances that cause (momentary) reductions in speed and affect traffic flow performance. Specifically, this project will aim to (1) characterize discernable differences in the car-following behavior of HDVs and CAVs of different classes under disturbances; and (2) elucidate their effects on traffic flow throughput and traffic flow stability. To this end, this research will develop a systematic method to bring together different control modeling paradigms for CAVs into a unifying framework to unveil their individual and collective impacts on traffic flow throughput and stability. Three CAV control paradigms will be considered in this study: linear control, model predictive control (MPC), and artificial-intelligence-based control. The vehicle-level investigation of complex interactions among CAVs and HDVs will unveil the interaction mechanisms and elucidate how they scale up to the collective behavior of traffic stream, which will inspire new modeling paradigms to describe mixed traffic flow dynamics and control CAVs.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.

互联和自动化的车辆（CAV）技术在私人行业，学术界，政府和公众中引起了极大的兴趣。当这些开创性的技术变得成熟时，将预测各种好处，包括提高道路效率，提高安全性以及更好的能源消耗和排放。但是，在技术足够成熟之前，这些好处将受到质疑。具体而言，福利的主要不确定性在于骑士和人类驱动的车辆（HDV）的混合交通，它们之间的相互作用在很大程度上是未知的。因此，在可预见的将来，流量可能与多种类别的CAV和HDV混合在一起。该项目将旨在更好地了解该混合交通系统的预期行为及其对流量的影响，以帮助充分利用CAV技术的潜力。结果将指导未来运输系统的交通管理策略，政策和长期计划的制定。该项目还将参与一系列综合研究，教育和外展活动，将其从这项研究获得的知识扩展到更广泛的受众，包括开发基于模拟的教育模块，组织研讨会，共享用于混合型本科生和研究生的模拟平台，并在研究和教育中造成较少的跨性别型号，并与HDV互动的次数不同。速度降低并影响交通流量的性能。具体而言，该项目的目的是（1）表征在干扰下不同类别的HDV和CAVS的驾驶员行为上的可分辨差异； （2）阐明它们对交通流量吞吐量和交通流量稳定性的影响。为此，这项研究将开发一种系统的方法，将骑士的不同控制建模范式整合到一个统一的框架中，以揭示其个人和集体对交通流量吞吐量和稳定性的影响。这项研究将考虑三个CAV控制范例：线性控制，模型预测控制（MPC）和基于人工智能的控制。 The vehicle-level investigation of complex interactions among CAVs and HDVs will unveil the interaction mechanisms and elucidate how they scale up to the collective behavior of traffic stream, which will inspire new modeling paradigms to describe mixed traffic flow dynamics and control CAVs.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.

项目成果

Processing, assessing, and enhancing the Waymo autonomous vehicle open dataset for driving behavior research

• DOI: 10.1016/j.trc.2021.103490
• 发表时间: 2022-01-01
• 期刊: TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
• 影响因子: 8.3
• 作者: Hu, Xiangwang;Zheng, Zuduo;Sun, Jian
• 通讯作者: Sun, Jian

A deep reinforcement learning based distributed control strategy for connected automated vehicles in mixed traffic platoon

• DOI: 10.1016/j.trc.2023.104019
• 发表时间: 2023-03
• 期刊: Transportation Research Part C: Emerging Technologies
• 作者: Haotian Shi;Danjue Chen;Nan Zheng;Xin Wang;Yang Zhou;Bin Ran

Modeling and Control Using Connected and Automated Vehicles with Chained Asymmetric Driver Behavior under Stop-and-Go Oscillations

使用具有链式不对称驾驶员行为的联网自动驾驶车辆在走走停停的振荡下进行建模和控制

• DOI: 10.1177/0361198120962494
• 发表时间: 2020
• 期刊: Transportation Research Record: Journal of the Transportation Research Board
• 作者: Srivastava, Anupam;Chen, Danjue;Ahn, Soyoung
• 通讯作者: Ahn, Soyoung

On multi-class automated vehicles: Car-following behavior and its implications for traffic dynamics

• DOI: 10.1016/j.trc.2021.103166
• 发表时间: 2021-07
• 期刊: Transportation Research Part C: Emerging Technologies
• 作者: Wissam Kontar;Tienan Li;A. Srivastava;Yang Zhou;Danjue Chen;Soyoung Ahn

Harnessing connected and automated vehicle technologies to control lane changes at freeway merge bottlenecks in mixed traffic

• DOI: 10.1016/j.trc.2020.102950
• 发表时间: 2021-02
• 作者: Danjue Chen;Soyoung Ahn