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

• 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

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.

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助。互联自动驾驶汽车（CAV）的潜在好处很多，社会期待这项技术将提高日常生活质量并兑现其承诺。然而，为了有效，必须对它们进行测试，以证明安全和安保的标准水平。运输系统的复杂性和相互关联性使得测试和核查任务极其困难，引起了对其安全和安保的严重关切。因此，它要求新的问题制定和一个新的系统的方法，CAV的测试和验证的任务。现有的测试解决方案使用特别的方法，例如行驶里程，来证明一些安全性指标，通常假设CAV对周围环境的感知是全面和理想的。然而，没有基本的结构已经开发出来证明CAV产品的安全性。该CAREER提案将运输系统建模为网络控制系统，提供了一种新的弹性度量，从而能够测试CAV的弹性。此外，它利用之前开发的验证框架将测试和验证过程制定为集中反馈控制系统，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事故并挽救生命。研究结果可以进一步应用于所有网络控制系统，例如高可靠性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，通过设计完全综合的教育模块和示范，提高学生，特别是代表人数不足的少数民族和妇女，对儿童援助委员会的安全问题的认识。我们计划包括以下活动，以满足农村和大部分经济贫困地区的需求：（一）开发针对小学，高中和大学生的课后在线STEM课程;（二）为教育工作者和行业合作伙伴提供研讨会，作为他们的专业发展活动;（三）通过研究本科生经验和实习计划，让代表性不足的本科生和大学生参与进来;（iv）开设本科生和高级研究生课程。这个CAREER项目解决CAV安全性的测试和验证问题。CAV的安全性的重要性已经在现有的文献中被认识到，并且已经激发了几种检测和补偿算法的开发，以确保在故障、失效和攻击下的安全性。然而，没有太多的努力投入到CAV的测试和验证任务。这个CAREER项目说明，目前的方法不足以在现实环境中安全地验证CAV的安全性，缺乏一个动态依赖的度量标准来衡量系统的弹性。我们描述了一个研究计划，其中的交通系统被建模为一个网络化的控制系统，道路，行人，车辆和交通标志（由于其动态行为）被建模为代理，相互使用传感器和通信网络。新的视角使我们能够提出一种新的弹性指标，与安全指标一起使用，以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是无限的，所提出的控制器制定了攻击的影响，而不是专注于特定的类型，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一种新的视角，从而能够开发一种独特的数据流生成器，该数据流生成器利用强化学习通过将测试过程建模为反馈控制系统来生成攻击，其中最小化安全性和安全性是期望的目标;以及（ii）开发一个独特的实验平台，利用混合现实（MR）和车辆在环（ViL）的力量，安全地测试CAV的安全性。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

Energy Efficiency of Connected Autonomous Vehicles: A Review

联网自动驾驶汽车的能源效率：回顾

• DOI: 10.3390/electronics12194086
• 发表时间: 2023
• 期刊: Electronics
• 影响因子: 2.9
• 作者: Faghihian, Hamed;Sargolzaei, Arman
• 通讯作者: Sargolzaei, Arman

Designing and Testing A Secure Cooperative Adaptive Cruise Control under False Data Injection Attack

设计和测试虚假数据注入攻击下的安全协作自适应巡航控制

• DOI: 10.1109/dsc61021.2023.10354170
• 发表时间: 2023
• 期刊: IEEE
• 作者: Cunningham-Rush, Jonas;Holland, James;Noei, Shirin;Sargolzaei, Arman
• 通讯作者: Sargolzaei, Arman

An Observer-Based Control for a Networked Control of Permanent Magnet Linear Motors under a False-Data-Injection Attack

• DOI: 10.1109/dsc61021.2023.10354110
• 发表时间: 2023-11
• 期刊: 2023 IEEE Conference on Dependable and Secure Computing (DSC)
• 作者: Parisa Ansari Bonab;James C. Holland;Arman Sargolzaei

A Testing and Verification Approach to Tune Control Parameters of Cooperative Driving Automation Under False Data Injection Attacks

• DOI: 10.1109/access.2024.3357357
• 发表时间: 2024
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: James C. Holland;Farahnaz Javidi-Niroumand;A. J. Alnaser;Arman Sargolzaei