  While perception systems in Connected and Autonomous Vehicles (CAVs), which
encompass both communication technologies and advanced sensors, promise to
significantly reduce human driving errors, they also expose CAVs to various
cyberattacks. These include both communication and sensing attacks, which
potentially jeopardize not only individual vehicles but also overall traffic
safety and efficiency. While much research has focused on communication
attacks, sensing attacks, which are equally critical, have garnered less
attention. To address this gap, this study offers a comprehensive review of
potential sensing attacks and their impact on target vehicles, focusing on
commonly deployed sensors in CAVs such as cameras, LiDAR, Radar, ultrasonic
sensors, and GPS. Based on this review, we discuss the feasibility of
integrating hardware-in-the-loop experiments with microscopic traffic
simulations. We also design baseline scenarios to analyze the macro-level
impact of sensing attacks on traffic flow. This study aims to bridge the
research gap between individual vehicle sensing attacks and broader macroscopic
impacts, thereby laying the foundation for future systemic understanding and
mitigation.
