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: 1931981
• 负责人: Javad Mohammadpour Velni
• 金额: $ 31.74万
• 依托单位: University of Georgia Research Foundation Inc
• 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: 2020-01-01 至 2023-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931981&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.

预计连接的自动化车辆（CAV）应用将改变运输景观并解决一些紧迫的安全性和效率问题。尽管通信和计算技术的进步使骑士的概念促进了这种系统的应用，控制和通信组件的耦合以及人类参与者的干扰，但对设计系统的设计系统构成了重大挑战，这些系统超出了原型环境。意识到CAV应用，特别是在人类可能部分留在循环中的情况下，需要解决人类投入引起的不确定性。大规模的骑士部署还将需要解决骑士和人类操作的系统协调行动方面的挑战。为了应对这些挑战，该项目开发了一种新型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形式描述和交流行为者的行为。然后将模型用于随机模型预测控制（SMPC）框架以最佳的操作协调。拟议的研究将通过三个主要影响领域提供广泛的社会利益：首先，通过推进混合系统的随机通信感知控制设计的研究；其次，通过开发新模型和高级控制器，以应对协调自动和载人车辆混合系统的新兴挑战，从而在其他涉及一般多代理系统的领域开放新的远景；第三，通过教育和外展活动，这些活动是这项多学科研究的自然扩展。该项目也是最近国家科学基金会/德意志Forschungs Gesellschaft（NSF/DFG）在网络物理系统（CPS）的最初成果。 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).由于人类的投入和参与者的交流造成的不确定性，此类设计目前具有挑战性。 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通过使用行业使用的模型进行广泛的数据驱动的共同模拟，以及一个逼真的小骑士和全面的原型CAV的结果。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来进行评估的。

项目成果

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

Impact of Information Flow Topology on Safety of Tightly-coupled Connected and Automated Vehicle Platoons Utilizing Stochastic Control

信息流拓扑对利用随机控制的紧耦合连接和自动化车队安全的影响

• DOI: 10.23919/ecc55457.2022.9837992
• 发表时间: 2022
• 期刊: European Control Conference (ECC
• 作者: Razzaghpour, Mahdi;Mosharafian, Sahand;Raftari, Arash;Mohammadpour Velni, Javad;Fallah, Yaser P.
• 通讯作者: Fallah, Yaser P.

Gaussian Process based Stochastic Model Predictive Control for Cooperative Adaptive Cruise Control

基于高斯过程的协同自适应巡航控制随机模型预测控制

• DOI: 10.1109/vnc52810.2021.9644629
• 发表时间: 2021
• 期刊: IEEE Vehicular Networking Conference (VNC
• 作者: Mosharafian, Sahand;Razzaghpour, Mahdi;Fallah, Yaser P.;Mohammadpour Velni, Javad
• 通讯作者: Mohammadpour Velni, Javad

Cooperative Adaptive Cruise Control in a Mixed-Autonomy Traffic System: A Hybrid Stochastic Predictive Approach Incorporating Lane Change

• DOI: 10.1109/tvt.2022.3202084
• 发表时间: 2023-01
• 期刊: IEEE Transactions on Vehicular Technology
• 影响因子: 6.8
• 作者: Sahand Mosharafian;Javad Mohammadpour Velni

Finite State Markov Modeling of C-V2X Erasure Links For Performance and Stability Analysis of Platooning Applications

• DOI: 10.1109/syscon53536.2022.9773892
• 发表时间: 2021-11
• 期刊: 2022 IEEE International Systems Conference (SysCon)
• 作者: Mahdi Razzaghpour;Adwait Datar;Daniel Schneider;Mahdi Zaman;H. Werner;Hannes Frey;J. Mohammadpour;Y. P. Fallah