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.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous VehiclesMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A Brief Survey on Autonomous Vehicle Possible Attacks, Exploits and Vulnerabilities
Advanced driver assistance systems are advancing at a rapid pace and all major companies started investing in developing the autonomous vehicles. But the security and reliability is still uncertain and debatable. Imagine…
Autonomous VehiclesSecuring Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward
Connected and autonomous vehicles (CAVs) will form the backbone of future next-generation intelligent transportation systems (ITS) providing travel comfort, road safety, along with a number of value-added services. Such …
Autonomous VehiclesBIG-bench Machine LearningIntegration of Vehicular Clouds and Autonomous Driving: Survey and Future Perspectives
For decades, researchers on Vehicular Ad-hoc Networks (VANETs) and autonomous vehicles presented various solutions for vehicular safety and autonomy, respectively. Yet, the developed work in these two areas has been most…
Autonomous DrivingAutonomous VehiclesMitigating Attacks on Artificial Intelligence-based Spectrum Sensing for Cellular Network Signals
Cellular networks (LTE, 5G, and beyond) are dramatically growing with high demand from consumers and more promising than the other wireless networks with advanced telecommunication technologies. The main goal of these ne…
ManagementSemantic Segmentation6G Cellular Networks and Connected Autonomous Vehicles
With 5G mobile communication systems been commercially rolled out, research discussions on next generation mobile systems, i.e., 6G, have started. On the other hand, vehicular technologies are also evolving rapidly, from…
Autonomous Vehicles