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

我们的世界目前正在经历令人难以置信的可用真实世界数据量的增长，但这些数据只在有限的一段时间内保持有用或有效。例如，在交通施工期间提供给司机的绕行信息在施工任务完成后失去其效用。该项目开发了确定数据库中积累的数据有效性的方法，以回答这样一个问题：数据什么时候到期？在物理世界中安全关键型应用程序的环境中，数据有效性的知识甚至更加重要：应该信任多少过去的数据来在当前做出安全关键型决策？是否可以相信来自附近地点的数据能够准确地反映当地的背景和条件？回答这些基本问题将影响到广泛的应用，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。在这个项目中开发的方法被实施和测试，用于在安全关键场景中控制联网的自动驾驶车辆，例如在可能结冰的道路上驾驶。这项工作具有巨大的潜力，不仅可以确保联网自动驾驶汽车即将部署的安全性，还可以提高广泛的数据和信息密集型应用的确定性和信心。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州洛厄尔大学的研究生和本科生研究人员的发展。该项目还包括以科学、技术、工程和数学(STEM)为重点的中学生外联活动，以扩大在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推移而衰减，以及在距离数据收集地点越来越远的情况下如何衰减。这项研究是在安全关键系统的背景下进行的，即在潜在结冰的道路上行驶的互联自动驾驶车辆(CAV)车队，其中安全关键的道路摩擦信息通过无线数据链路共享到一个协调数据平均的中央时空数据库。这些数据被用来估计道路摩擦系数(即冰的存在)，并被传输到附近的其他连接的车辆。使用Allan方差分析评估数据库内数据信任的持续时间和摩擦估计的有效性，使数据库能够对数据的及时性和质量进行内部建模和监测。研究人员还研究了耦合的快慢反馈回路的性能指标(例如，稳定性)，其中快回路在车辆层面工作，以使用数据库中介的摩擦测量预览确保CAV在结冰条件下的安全运行。慢循环是使用骑士队提供的当前测量数据在数据库中对空间、多车辆数据进行平均。然后，在CAV车队在具有大时空范围的道路网络上运行的背景下，对这些功能进行审查。虽然这些方法在CAV环境中实施，但可以用于从空间、时间或时空数据流中合成可操作信息的任何应用程序。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。

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.