Arman Sargolzaei’s research while affiliated with University of South Florida and other places

The combination of connectivity and automation allows connected and autonomous vehicles (CAVs) to operate autonomously using advanced on-board sensors while communicating with each other via vehicle-to-vehicle (V2V) technology to enhance safety, efficiency, and mobility. One of the most promising features of CAVs is cooperative adaptive cruise control (CACC). This system extends the capabilities of conventional adaptive cruise control (ACC) by facilitating the exchange of critical parameters among vehicles to enhance safety, traffic flow, and efficiency. However, increased connectivity introduces new vulnerabilities, making CACC susceptible to cyber-attacks, including false data injection (FDI) attacks, which can compromise vehicle safety. To address this challenge, we propose a secure observer-based control design leveraging Lyapunov stability analysis, which is capable of mitigating the adverse impact of FDI attacks and ensuring system safety. This approach uniquely addresses system security without relying on a known lead vehicle model. The developed approach is validated through simulation results, demonstrating its effectiveness.

Connected and Autonomous Vehicles (CAVs) have the potential to revolutionize transportation by addressing critical challenges such as safety, energy efficiency, traffic congestion, and environmental impact. Realizing these benefits, however, requires the development of a rigorous testing and verification framework to enable the safe, efficient, and reliable deployment of CAVs across diverse operational scenarios. Despite the growing body of research, there remains a significant gap in review papers that comprehensively summarize recent studies related to the testing and verification of CAVs while identifying current challenges and highlighting future research directions. This paper seeks to address this gap by presenting a comprehensive review of the existing testing and verification frameworks for CAVs and identifying their associated challenges. Key topics covered include scenario generation, verification cost functions, assertion values, and security considerations. Furthermore, the paper highlights limitations within current frameworks, emphasizing the gaps that hinder systematic and comprehensive evaluations.

div>Connected and autonomous vehicles (CAVs) rely on communication channels to improve safety and efficiency. However, this connectivity leaves them vulnerable to potential cyberattacks, such as false data injection (FDI) attacks. We can mitigate the effect of FDI attacks by designing secure control techniques. However, tuning control parameters is essential for the safety and security of such techniques, and there is no systematic approach to achieving that. In this article, our primary focus is on cooperative adaptive cruise control (CACC), a key component of CAVs. We develop a secure CACC by integrating model-based and learning-based approaches to detect and mitigate FDI attacks in real-time. We analyze the stability of the proposed resilient controller through Lyapunov stability analysis, identifying sufficient conditions for its effectiveness. We use these sufficient conditions and develop a reinforcement learning (RL)-based tuning algorithm to adjust the parameter gains of the controller, observer, and FDI attack estimator, ensuring the safety and security of the developed CACC under varying conditions. We evaluated the performance of the developed controller before and after optimizing parameters, and the results show about a 50% improvement in accuracy of the FDI attack estimation and a 76% enhancement in safe following distance with the optimized controller in each scenario.</div

Cooperative adaptive cruise control (CACC) is one of the many advanced driver assistance systems (ADAS) that leverage communication between nearby vehicles to maintain speed while ensuring safe following distances. The current CACC algorithms are designed with the assumption that the communication channel is secure. However, the use of communication channels makes CACC susceptible to attacks, such as False data injection (FDI). This paper develops a novel secure nonlinear controller and a nonlinear observer which can estimate FDI attacks in real-time. Furthermore, this paper shows that the developed controller and FDI attack estimation techniques ensure semi-globally uniformly bounded tracking under FDI attacks, noise, and disturbances. The efficaciousness of the proposed CACC algorithm was demonstrated in simulation and through experimental implementation. During testing using both methodologies, the controller was able to maintain a safe following distance and estimate FDI attacks and noise in real-time.

Digital world has produced efficiencies, new innovative products, and close customer relationships globally by the effective use of mobile, IoT (Internet of Things), social media, analytics and cloud technology to generate models for better decisions. Blockchain is recently introduced and revolutionizing the digital world bringing a new perspective to security, resiliency and efficiency of systems. While initially popularized by Bitcoin, Blockchain is much more than a foundation for crypto currency. It offers a secure way to exchange any kind of good, service, or transaction. Industrial growth increasingly depends on trusted partnerships; but increasing regulation, cybercrime and fraud are inhibiting expansion. To address these challenges, Blockchain will enable more agile value chains, faster product innovations, closer customer relationships, and quicker integration with the IoT and cloud technology. Further Blockchain provides a lower cost of trade with a trusted contract monitored without intervention from third parties who may not add direct value. It facilitates smart contracts, engagements, and agreements with inherent, robust cyber security features. This paper is an effort to break the ground for presenting and demonstrating the use of Blockchain technology in multiple industrial applications. A healthcare industry application, Healthchain, is formalized and developed on the foundation of Blockchain using IBM Blockchain initiative. The concepts are transferable to a wide range of industries as finance, government and manufacturing where security, scalability and efficiency must meet.