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

该奖项的全部或部分资金来自《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，它们必须经过测试，以证明安全和安保的标准水平。运输系统的复杂和相互关联的性质使测试和核查任务变得极其困难，引起了对其安全和安保的严重关切。因此，它要求对CAV测试和验证任务提出新的问题和新的系统方法。现有的测试解决方案使用特定的方法，例如行驶里程，来证明一些安全迹象，通常假设CAV对周围环境的感知是全面和理想的。然而，还没有开发出基本的结构来证明CAV产品的安全性。这份职业建议书将运输系统建模为一个网络控制系统，提供了一种新的弹性度量，使Cavs能够测试弹性。此外，它利用先前开发的验证框架将测试和验证过程描述为能够开发新型攻击生成器的集中反馈控制系统。该项目的预期结果将为安全测试CAV铺平道路，直接影响这项技术和相关标准的未来，最终消除与碰撞相关的死亡和拯救生命。这些研究成果可以进一步应用于所有网络控制系统，如高保证的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整合的教育单元和示范，提高学生，特别是代表不足的学生和妇女少数群体对CAV安全的认识。我们计划包括以下活动，以满足农村和大部分经济困难地区的需求：(I)为小学、高中和大学生制定课外在线STEM课程；(Ii)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；(Iii)通过研究本科生和实习计划，让未被充分代表的本科生和大学生参与；(Iv)开发本科生和高级研究生课程。这个职业项目解决了Cavs安全的测试和验证问题。现有文献已经认识到CAV安全的重要性，并推动了几种检测和补偿算法的发展，以确保在故障、故障和攻击下的安全。然而，在CAVS测试和验证任务上投入的精力并不多。这个职业项目表明，当前的方法不足以在现实环境中安全地验证CAV的安全性，因为缺乏一个动态依赖的衡量标准来衡量系统的弹性。我们描述了一个研究计划，其中交通系统被建模为一个网络控制系统，其中道路、行人、车辆和交通标志(由于它们的动态行为)被建模为智能体，利用传感器和通信网络相互作用。新的视角允许我们提出一种新的弹性度量，与安全度量一起使用，以开发基于强化学习的控制器，用于测试骑士队的安全性。由于故障和攻击的类型是无限的，所提出的控制器将攻击的影响公式化，而不是关注特定的类型，从而简化了故障和攻击的生成过程。该项目预计将通过以下方式推进CAV的测试和验证领域：(I)引入使用网络控制系统概念的新视角，通过将测试过程建模为将安全性和安全性降至最低的反馈控制系统，利用强化学习来开发独特的数据流生成器来生成攻击，以及(Ii)开发一个独特的实验平台，该平台丰富了混合现实(MR)和车辆在环(VIL)的能力，以安全地测试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。

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.