CPS: Medium: Collaborative Research: Automated Discovery of Data Validity for Safety-Critical Feedback Control in a Population of Connected Vehicles

CPS：中：协作研究：自动发现联网车辆中安全关键反馈控制的数据有效性

• 批准号: 1931927
• 负责人: Craig Beal
• 金额: $ 12.41万
• 依托单位: Bucknell University
• 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: 2019-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931927&HistoricalAwards=false
• 关键词: CPS; Medium; Collaborative; Research; Automated

Our world is currently experiencing an incredible increase in the amount of real-world data available, yet that data remains useful or valid only for a finite period of time. For example, detour information provided to drivers during traffic construction loses its utility upon completion of the construction assignment. This project develops methods to determine the validity of data accumulated in databases, to answer the question: when do data expire? Knowledge of data validity is even more important in the context of safety-critical applications in the physical world: how much of the past data should be trusted to make safety-critical decisions in the present? Can data from nearby locations be trusted to accurately reflect local context and conditions? Answering these fundamental questions will impact a wide-range of applications, including traffic management, national defense, weather forecasting, etc., since data is a universal feature of modern society. The methods developed in this project are implemented and tested for control of connected autonomous vehicles in safety-critical scenarios such as driving on potentially icy roads. This work has significant potential to not only ensure safety in the imminent deployment of connected autonomous vehicles, but also improve certainty and confidence in a wide range of data- and information- intensive applications. This collaborative research will support development of graduate and undergraduate researchers at Penn State University, Bucknell University, and the University of Massachusetts Lowell. The project also includes Science, Technology, Engineering, and Math (STEM)-focused outreach activities for middle-school students to broaden participation within the field of cyber-physical systems. The research objective of the project is to create methods to determine how the validity of data decays over time, and over increasing distances away from where the data was collected. The research is conducted in the context of safety-critical systems, namely fleets of connected autonomous vehicles (CAVs) driving on potentially icy roads, where safety-critical road friction information is shared via a wireless data link to a central spatiotemporal database that mediates data averaging. This data is used to estimate the roadway friction coefficient (i.e. the presence of ice) and is transmitted to other connected vehicles in the vicinity. The time duration of data trust and validity of the friction estimates within the database are evaluated using Allan variance analysis, enabling the database to internally model and monitor data timeliness and quality. The investigators also study performance metrics (e.g., stability) of the coupled fast and slow feedback loops, where the fast loop acts at the vehicle level to ensure safe CAV operations in icy conditions using database-mediated preview of friction measurements. The slow loop is the spatial, multi-vehicle data averaging in the database using current measurements provided by a fleet of CAVs. These functionalities are then examined in the context of CAV fleets operating on road networks with large spatiotemporal extents. While implemented in a CAV context, these methods can be used in any application that synthesizes actionable information from spatial, temporal, or spatiotemporal data streams.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.

我们的世界目前正在经历可用的现实数据数量的不可思议，但是数据仍然有用或仅在有限的时间段内有效。例如，交通建设期间提供给驾驶员的绕道信息在建设任务完成后失去了效用。该项目开发了确定数据库中累积数据的有效性的方法，以回答以下问题：数据何时到期？在物理世界中的关键安全应用中，对数据有效性的了解更为重要：过去的数据应该值得信赖以在当前做出安全关键决策？可以信任附近位置的数据以准确反映本地环境和条件吗？回答这些基本问题将影响广泛的应用程序，包括交通管理，国防，天气预报等，因为数据是现代社会的普遍特征。该项目中开发的方法进行了测试，以控制安全至关重要的情况，例如在潜在的冰冷道路上行驶。这项工作具有巨大的潜力，不仅可以确保即将部署连接的自动驾驶汽车的安全性，而且还提高了对广泛的数据和信息密集型应用程序的确定性和信心。这项合作研究将支持宾夕法尼亚州立大学，巴克内尔大学和马萨诸塞大学洛厄尔大学的研究生和本科研究人员的发展。该项目还包括科学，技术，工程和数学（STEM），专注于中学生，以扩大网络物理系统领域的参与。该项目的研究目标是创建方法，以确定数据的有效性如何随着时间的流逝而衰减，并随着距离收集数据的距离而增加的距离。这项研究是在安全关键系统的背景下进行的，即在潜在的冰冷道路上行驶的连接自动驾驶汽车（CAVS）的舰队，在该道路上，通过无线数据链接共享了与中央时空数据库的无线数据链接共享，从而介导数据平均。该数据用于估计道路摩擦系数（即冰的存在），并将其传输到附近的其他连接车辆。使用Allan方差分析评估了数据库中数据信任的持续时间和摩擦估计的有效性，从而使数据库能够内部建模并监视数据及时性和质量。研究人员还研究了耦合快速和缓慢的反馈回路的性能指标（例如稳定性），其中快速循环在车辆水平上起作用，以确保使用数据库介导的摩擦测量预览在冰冷条件下安全的CAV操作。缓慢的循环是使用骑士队提供的电流测量值在数据库中平均的空间，多车程数据。然后在具有较大时空范围的道路网络上运行的CAV舰队中检查这些功能。尽管在CAV上下文中实施，但这些方法可用于任何应用程序中，该应用程序从空间，时间或时空数据流中综合了可行的信息。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影响的审查标准来通过评估来进行评估的。

项目成果

A Micro-simulation Framework for Studying CAVs Behavior and Control Utilizing a Traffic Simulator, Chassis Simulation, and a Shared Roadway Friction Database

利用交通模拟器、底盘模拟和共享道路摩擦数据库研究 CAV 行为和控制的微观模拟框架

• DOI: 10.23919/acc50511.2021.9483221
• 发表时间: 2021
• 期刊: 2021 American Control Conference (ACC
• 作者: Gao, Liming;Maddipatla, Srivenkata Satya;Beal, Craig;Jerath, Kshitij;Chen, Cindy;Sinanaj, Lorina;Haeri, Hossein;Brennan, Sean
• 通讯作者: Brennan, Sean

Allan Variance-based Granulation Technique for Large Temporal Databases

• DOI: 10.5220/0010651500003064
• 发表时间: 2021
• 作者: Lorina Sinanaj;H. Haeri;Liming Gao;Srivenkata Satya Prasad Maddipatla;Cindy Chen;Kshitij Jerath;C. Beal;Sean Brennan

Vehicle Model Predictive Trajectory Tracking Control with Curvature and Friction Preview

• DOI: 10.1016/j.ifacol.2022.10.288
• 发表时间: 2022
• 期刊: IFAC-PapersOnLine
• 作者: Liming Gao;C. Beal;Juliette Mitrovich;Sean Brennan

Boxes-Based Representation and Data Sharing of Road Surface Friction for CAVs

• DOI: 10.1007/s42421-023-00071-0
• 发表时间: 2023-06
• 期刊: Data Science for Transportation
• 作者: Liming Gao;Juliette Mitrovich;C. Beal;Wushuang Bai;S. Maddipatla;Cindy Chen;Kshitij Jerath;H. Haeri;Lorina Sinanaj;Sean Brennan

Granulation of Large Temporal Databases: An Allan Variance Approach

• DOI: 10.1007/s42979-022-01397-2
• 发表时间: 2022-10
• 期刊: SN Computer Science
• 作者: Lorina Sinanaj;H. Haeri;S. Maddipatla;Liming Gao;Rinith Pakala;Niket Kathiriya;Craig Beal;Sean Brennan;Cindy Chen;Kshitij Jerath