Overview of Sensing Attacks on Autonomous Vehicle Technologies and Impact on Traffic Flow

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

Machine learning and blockchain technologies for cybersecurity in connected vehicles

Future connected and autonomous vehicles (CAVs) must be secured againstcyberattacks for their everyday functions on the road so that safety of passengersand vehicles can be ensured. This article presents a holistic review of cybersecurityattacks on sensors and threats regardingmulti-modal sensor fusion. A compre-hensive review of cyberattacks on intra-vehicle and inter-vehicle communicationsis presented afterward. Besides the analysis of conventional cybersecurity threatsand countermeasures for CAV systems,a detailed review of modern machinelearning, federated learning, and blockchain approach is also conducted to safe-guard CAVs. Machine learning and data mining-aided intrusion detection systemsand other countermeasures dealing with these challenges are elaborated at theend of the related section. In the last section, research challenges and future direc-tions are identified