CPS: DFG Joint: Medium: Collaborative Research: Perceptive Stochastic Coordination in Mass Platoons of Automated Vehicles

CPS：DFG 联合：媒介：协作研究：自动车辆大规模排中的感知随机协调

• 批准号: 2302215
• 负责人: Javad Mohammadpour Velni
• 金额: $ 31.74万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2302215&HistoricalAwards=false
• 关键词: CPS; DFG; Joint; Medium; Collaborative

Connected Automated Vehicle (CAV) applications are expected to transform the transportation landscape and address some of the pressing safety and efficiency issues. While advances in communication and computing technologies enable the concept of CAVs, the coupling of application, control and communication components of such systems and interference from human actors, pose significant challenges to designing systems that are safe and reliable beyond prototype environments. Realizing CAV applications, in particular in situations where humans may partly remain in the loop, requires addressing uncertainties that arise from human input. Large scale deployment of CAVs will also require addressing challenges in coordination of actions among CAVs and with human operated systems. To address these challenges, this project develops a novel model-based stochastic hybrid systems (SHS)-theoretic approach that relies on describing and communicating behaviors of actors in the system in the form of evolving SHS using Bayesian learning. The models are then utilized in a stochastic model predictive control (SMPC) framework for optimal coordination of actions. The proposed research will provide wide-ranging societal benefits through three major impact areas: first, by advancing research in stochastic communication-aware control design for hybrid systems; second, through the development of new models and advanced controllers to address the emerging challenges of coordinating mixed systems of automated and manned vehicles, hence opening new vistas in other areas involving general multi-agent systems; and third, through educational and outreach activities that are natural extensions of this multidisciplinary research. This project is also the first fruits of a recent National Science Foundation/Deutsche Forschungs Gesellschaft (NSF/DFG) collaboration on cyber-physical systems (CPS). Through this collaboration, NSF funds the US component (University of Central Florida and University of Georgia) while the German partners (University of Technology and University of Koblenz-Landau) are funded by DFG.The overarching goal of this collaborative research is to introduce SHS-based modeling and control concepts to allow the design of highly efficient CAV systems capable of large scale coordination (mass platooning). Such designs are currently challenging due to the uncertainties that stem from human input and communication of actors. The key objectives of the project are to: (1) develop methods for capturing the human, sensors and communication induced uncertainties of mixed automated and manned systems in a stochastic hybrid system form (perception maps) and communicating them in a control-aware fashion, (2) employ the models in an SMPC framework to produce multi-modal decisions and lower level longitudinal motion control in a single unified framework, and (3) validate the analytical outcomes through both extensive data-driven co-simulation using industry utilized models, and a fleet of realistic small CAVs and a full scale prototype CAV.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.

互联自动驾驶汽车（CAV）应用有望改变交通格局，并解决一些紧迫的安全和效率问题。虽然通信和计算技术的进步使CAV的概念成为可能，但这种系统的应用、控制和通信组件的耦合以及人类行为者的干扰，对设计超越原型环境的安全可靠的系统提出了重大挑战。实现CAV应用，特别是在人类可能部分留在回路中的情况下，需要解决由人类输入引起的不确定性。CAV的大规模部署还需要解决CAV之间以及与人类操作系统的协调行动方面的挑战。为了解决这些挑战，该项目开发了一种新的基于模型的随机混合系统（SHS）理论方法，该方法依赖于使用贝叶斯学习以不断发展的SHS的形式描述和交流系统中参与者的行为。然后，利用随机模型预测控制（SMPC）框架的最佳协调行动的模型。拟议的研究将通过三个主要影响领域提供广泛的社会效益：第一，通过推进混合动力系统随机通信感知控制设计的研究;第二，通过开发新模型和先进控制器，以应对协调自动化和有人驾驶车辆混合系统的新挑战，从而在涉及一般多智能体系统的其他领域开辟新的前景。第三，通过教育和推广活动，这是这种多学科研究的自然延伸。该项目也是最近美国国家科学基金会/德国研究协会（NSF/DFG）在网络物理系统（CPS）方面合作的第一批成果。通过这项合作，NSF资助美国的部分（中央佛罗里达大学和格鲁吉亚大学），而德国的合作伙伴（科技大学和科布伦茨-兰道大学）由DFG资助。这项合作研究的首要目标是引入基于SHS的建模和控制概念，以允许设计能够进行大规模协调（大规模编队）的高效CAV系统。这种设计目前具有挑战性，因为人类输入和参与者的沟通产生了不确定性。该项目的主要目标是：（1）开发用于捕获随机混合系统形式的混合自动化和人工系统中由人、传感器和通信引起的不确定性的方法（感知图）并以控制感知的方式传达它们，（2）在SMPC框架中采用模型以在单个统一框架中产生多模态决策和较低级别的纵向运动控制，以及（3）通过使用工业使用的模型进行广泛的数据驱动的联合仿真，以及一队真实的小型CAV和全尺寸原型CAV来验证分析结果。该奖项反映了NSF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

A hybrid stochastic model predictive design approach for cooperative adaptive cruise control in connected vehicle applications

• DOI: 10.1016/j.conengprac.2022.105383
• 发表时间: 2023-01
• 期刊: Control Engineering Practice
• 影响因子: 4.9
• 作者: Sahand Mosharafian;Javad Mohammadpour Velni

Scenario-Based Hybrid Model Predictive Design for Cooperative Adaptive Cruise Control in Mixed-Autonomy Environments

• DOI: 10.1109/cdc49753.2023.10383460
• 发表时间: 2023-12
• 期刊: 2023 62nd IEEE Conference on Decision and Control (CDC)
• 作者: Sahand Mosharafian;Yajie Bao;Javad Mohammadpour

Leveraging autonomous vehicles in mixed-autonomy traffic networks with reinforcement learning-controlled intersections

• DOI: 10.1080/19427867.2022.2146302
• 发表时间: 2022-11
• 期刊: Transportation Letters
• 作者: Sahand Mosharafian;Shirin Afzali;Javad Mohammadpour Velni