The promises of highly automated transportation systems (HATS) are clear and compelling: a path to zero roadway fatalities, low-cost mobility of people and goods, broadening access to transportation services. But, HATS will fail to gain the public’s trust if they are seen as uniquely vulnerable to cyberattacks. Thus, it is imperative to proactively perform cybersecurity risk analysis for critical components in the latest HATS technology, especially the relatively new ones such as the AI-enabled vehicle autonomy, so that their potential cybersecurity problems can be sufficiently identified, understood, and ideally addressed before wide deployment. In this 1-year project period, our goal is to perform the first large-scale study of the cybersecurity risks to HATS autonomy stack from the commercial systems perspective. Specifically, while various prior works have studied the security vulnerabilities on the HATS autonomy stack (e.g., on critical HATS autonomy stack components such as perception, localization, prediction, planning, and control), almost all of them only focused on studying such security problems in academic settings, e.g., on open-source HATS autonomy models/systems released by and/or designed for academic research community. However, these models/systems are not directly used in any commercial products, which thus makes the impacts of these security problems on real-world commercial HATS systems unclear. A few recent works have considered performing evaluation against commercial HATS systems, but so far they are all limited to one single commercial product without systematically considering the representativeness of such a system, making the generalizability of their findings highly questionable. In this project, we thus aim at filling this critical research gap by performing the first large-scale study of existing security research in this problem space against real-world commercial HATS systems. In this 1-year period, we plan to target a few commercial HATS autonomy features that both have high accessibility in consumer products today (e.g., in common consumer vehicles) and also have high representativeness in the HATS autonomy stack security research area (e.g., those heavily studied by existing security research), for example the traffic sign recognition (TSR), automatic lane centering (ALC), and adaptive cruise control (ACC) features.