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

联网和自动驾驶汽车（CAV）技术已经引起了私营企业、学术界、政府和公众的极大兴趣。当这些突破性技术成熟时，预计会带来广泛的好处，包括更高的道路效率、更好的安全性、更好的能源消耗和排放。然而，在技术充分成熟之前，这些好处还有待商榷。具体来说，效益的主要不确定性在于自动驾驶汽车和人类驾驶汽车（HDVs）的混合交通，它们之间的相互作用在很大程度上仍然未知。因此，在可预见的未来，交通可能会混合多种类型的cav和hcv。该项目旨在更好地了解这种混合交通系统的预期行为及其对交通的影响，以帮助充分利用自动驾驶汽车技术的潜力。研究结果将指导交通管理战略、政策的制定以及未来交通系统的长期规划。该项目还将参与一系列综合研究、教育和推广活动，将从这项研究中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、共享混合交通的模拟平台，以及吸引本科生和研究生，特别是代表性不足的群体参与研究和教育。本研究旨在了解hdv和不同类别的cav如何在交通干扰下相互作用，导致（瞬间）速度降低并影响交通流性能。具体而言，本项目将致力于(1)表征不同类别的hcv和cav在干扰下跟随汽车行为的可识别差异；(2)阐明其对交通流吞吐量和交通流稳定性的影响。为此，本研究将开发一种系统的方法，将不同的自动驾驶汽车控制建模范式整合到一个统一的框架中，以揭示它们对交通流吞吐量和稳定性的个体和集体影响。本研究将考虑三种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。对自动驾驶汽车和hdv之间复杂交互的车辆级研究将揭示交互机制，并阐明它们如何扩展到交通流的集体行为，这将激发新的建模范式来描述混合交通流动力学和控制自动驾驶汽车。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。

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.