CAREER: Systematic Approach for Extensively (SAfEly) Testing and Verifying the Security of Connected and Autonomous Vehicle

职业：广泛（安全）测试和验证联网自动驾驶汽车安全性的系统方法

基本信息

批准号：
2241718
负责人：
Arman Sargolzaei
金额：
$ 50万
依托单位：
University of South Florida
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-01 至 2027-02-28
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2241718&HistoricalAwards=false
关键词：
CAREER Systematic Approach Extensively SAfEly

项目摘要

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).The potential benefits of connected autonomous vehicles (CAV) are numerous, and society is expecting that this technology will increase the quality of everyday life and follow through on its promises. However, to be effective, they must be tested to demonstrate a standard level of safety and security. The complex and interconnected nature of the transportation system makes the task of testing and verification exceedingly difficult, raising serious concerns regarding their safety and security. It, thus, calls for new problem formulation and a novel systematic approach for the task of CAV testing and verification. The existing testing solutions use ad-hoc methods, such as miles driven, to demonstrate some indication of safety, often assuming that the CAV's perception of the surrounding environment is comprehensive and ideal. However, no fundamental structure has been developed to demonstrate the security of CAV products. This CAREER proposal models the transportation system as a networked control system providing a novel resiliency metric enabling the testing resiliency of CAVs. In addition, it utilizes the prior developed verification framework to formulate the testing and verification process as a centralized feedback control system enabling the development of a novel attack generator. The expected outcomes of this project would pave the way towards safely testing CAVs, directly impacting the future of this technology and related standards, ultimately eliminating crash-related fatalities and saving lives. The research findings can be further implemented for all networked control systems, such as high-assurance military systems and autonomous systems ranging from unmanned aerial vehicles to power systems. The educational purpose of the project is to expand students', particularly underrepresented and women minorities, awareness of CAV security by designing fully integrated educational modules and demonstrations. We plan to include the following activities to serve the need for rural and largely economically distressed regions: (i) develop after school online STEM curriculum adjusted for primary, High-school, and college students; (ii) provide workshops for educators and industrial partners as their professional development activities; (iii) involve underrepresented undergraduate and college students through research for undergraduate experience and internship program; (iv) develop an undergraduate and an advanced graduate courses.This CAREER project addresses the problem of testing and verification for the security of CAVs. The importance of the security of CAVs has been recognized in the existing literature and has motivated the development of several detection and compensation algorithms to ensure safety under faults, failures, and attacks. However, not much effort is invested in the task of CAVs testing and verification. This CAREER project illustrates that the current approaches are insufficient to safely verify the security of CAVs in a realistic environment, suffering from the lack of a metric that is dynamic-dependent to measure the system resiliency. We describe a research plan where a transportation system is modeled as a networked control system where roads, pedestrians, vehicles, and traffic signs (due to their dynamic behavior) are modeled as agents, interacting with each other using sensors and communication networks. The new perspective allows us to propose a novel resiliency metric to be used alongside the safety metric to develop reinforcement learning-based controllers for testing CAVs' security. As there are infinite types of faults and attacks, the proposed controller formulates the effects of attacks rather than focusing on specific types, easing the process of fault and attack generation. This project is expected to advance the area of testing and verification of CAVs by (i) Introducing a novel perspective using the concept of networked control systems enabling the development of a unique data stream generator utilizing reinforcement learning to generate attacks by modeling the testing process as a feedback control system where minimizing safety and security is the desired objective and (ii) Developing a unique experimental platform enriched with the power of mixed reality (MR) and vehicle-in-the-loop (ViL) to test the security of CAVs safely.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.

该奖项的全部或部分资金来源于《2021 年美国救援计划法案》（公法 117-2）。联网自动驾驶汽车 (CAV) 的潜在好处众多，社会期望这项技术将提高救援质量日常生活并履行其承诺。然而，为了有效，它们必须经过测试以证明标准的安全水平。交通运输系统的复杂性和相互关联性使得测试和验证任务变得异常困难，引发了对其安全性的严重担忧。因此，它需要新的问题表述和新颖的系统方法来完成 CAV 测试和验证的任务。现有的测试解决方案使用临时方法（例如行驶里程）来展示一些安全指示，通常假设 CAV 对周围环境的感知是全面且理想的。然而，尚未开发出任何基本结构来证明 CAV 产品的安全性。该职业提案将运输系统建模为网络控制系统，提供了一种新颖的弹性指标，可以测试 CAV 的弹性。此外，它利用先前开发的验证框架将测试和验证过程制定为集中式反馈控制系统，从而能够开发新型攻击生成器。该项目的预期成果将为安全测试 CAV 铺平道路，直接影响该技术和相关标准的未来，最终消除与碰撞相关的死亡并挽救生命。研究结果可以进一步应用于所有网络控制系统，例如高保证军事系统和从无人机到电力系统的自主系统。该项目的教育目的是通过设计完全集成的教育模块和演示来提高学生，特别是代表性不足的学生和女性少数群体对 CAV 安全的认识。我们计划开展以下活动，以满足农村和经济困难地区的需求： (i) 开发针对小学、高中和大学生的课后在线 STEM 课程； (ii) 为教育工作者和工业伙伴提供讲习班，作为他们的专业发展活动； (iii) 通过本科生经验研究和实习计划让代表性不足的本科生和大学生参与进来； (iv) 开发本科生和高级研究生课程。该职业项目解决 CAV 安全性的测试和验证问题。现有文献已经认识到 CAV 安全性的重要性，并推动了多种检测和补偿算法的开发，以确保在故障、故障和攻击下的安全。然而，在CAV的测试和验证任务上投入的精力并不多。该 CAREER 项目表明，由于缺乏动态依赖的衡量系统弹性的指标，当前的方法不足以在现实环境中安全地验证 CAV 的安全性。我们描述了一项研究计划，其中交通系统被建模为网络控制系统，其中道路、行人、车辆和交通标志（由于其动态行为）被建模为代理，使用传感器和通信网络相互交互。新的视角使我们能够提出一种新颖的弹性指标，与安全指标一起使用来开发基于强化学习的控制器来测试 CAV 的安全性。由于故障和攻击的类型无限，所提出的控制器制定了攻击的影响，而不是关注特定类型，从而简化了故障和攻击生成的过程。该项目预计将通过以下方式推进 CAV 的测试和验证领域：(i) 使用网络控制系统的概念引入新颖的视角，从而能够开发独特的数据流生成器，利用强化学习通过将测试过程建模为生成攻击反馈控制系统，其中最大程度地降低安全性和安保性是期望的目标；(ii) 开发一个独特的实验平台，该平台富含混合现实 (MR) 和车辆在环 (ViL) 的功能，以安全地测试 CAV 的安全性.这个奖项体现了通过使用基金会的智力价值和更广泛的影响审查标准进行评估，NSF 的法定使命被认为值得支持。