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

Tennessee Technological University, Cookeville TN

Investigators

Abstract

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.