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

该研究项目将为互联自动车辆和传统人工驾驶车辆的混合交通开发协作排队算法。最近互联和自动化车辆技术的发展使一组这样的车辆能够以安全的方式紧贴着行驶(称为协作排队)，这大大提高了机动性和能源效率。然而，当群内存在人驾驶车辆时，由于不确定的人类驾驶员行为，车辆排的凝聚力是不可能的。该项目将开发的新型协同排队算法将使联网和自动化车辆能够以更短的间隔安全地跟随人类驾驶的车辆，同时缓解交通干扰。这些算法还将能够通过补充人类不完美的行为，在联网但非自动化的车辆中帮助人类驾驶员。因此，在联网自动车辆的市场渗透率较低的情况下，合作排队可以有效地运行。这项研究将改善地面交通的机动性，减少温室气体排放，从而造福于国家经济福利和公众健康。这项研究将使运输工程、网络物理系统、控制理论和机械工程方面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。互联自动车辆(CAV)已经能够以短距离成排稳定行驶，从而提高了机动性和能源效率。然而，它在Cavs与非Cavs交互的混合流量中无法有效工作。本研究的目标是开发和验证混合交通中的协同式自适应巡航控制(CACC-MT)，该控制能够安全、高效地稳定包括CAV、传统车辆和互联的人驾驶车辆在内的混合交通。CACC-MT使CAV能够在前一辆车未连接时，使用从另一前一辆车接收的信息来执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还包括人在环CACC算法，该算法能够根据从之前连接的车辆接收的信息来协同驾驶人类驾驶员，并帮助车辆在交通混乱中更平稳和安全地行驶。由于CACC-MT采用稳健的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，它也确保了混合交通的合作排成安全高效，而不需要任何人类驾驶员的先验知识。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.