Location-Based Schemes for Mitigating Cyber Threats on Connected and Automated Vehicles: A Survey and Design Framework

Suo, Dajiang; Moore, John M.; Boesch, Mathew; Post, Kyle

doi:10.1109/tits.2020.3038755

Cited by 12 publications

(2 citation statements)

References 95 publications

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“…RANSAC requires high computational capability to formulate the momentum model for adversarial detection [8]. The approach presented in [12] introduces extra modulation components into the lens system and may decrease the sensitivity of LiDAR [19]. A vehicle system is usually equipped with more than one sensors to estimate states and observe its surroundings.…”

Section: Related Workmentioning

confidence: 99%

Cooperative Perception for Safe Control of Autonomous Vehicles under LiDAR Spoofing Attacks

Zhang¹,

Li²,

Cheng³

et al. 2023

Proceedings Inaugural International Symposium on Vehicle Security &Amp; Privacy

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Autonomous vehicles rely on LiDAR sensors to detect obstacles such as pedestrians, other vehicles, and fixed infrastructures. LiDAR spoofing attacks have been demonstrated that either create erroneous obstacles or prevent detection of real obstacles, resulting in unsafe driving behaviors. In this paper, we propose an approach to detect and mitigate LiDAR spoofing attacks by leveraging LiDAR scan data from other neighboring vehicles. This approach exploits the fact that spoofing attacks can typically only be mounted on one vehicle at a time, and introduce additional points into the victim's scan that can be readily detected by comparison from other, non-modified scans. We develop a Fault Detection, Identification, and Isolation procedure that identifies non-existing obstacle, physical removal, and adversarial object attacks, while also estimating the actual locations of obstacles. We propose a control algorithm that guarantees that these estimated object locations are avoided. We validate our framework using a CARLA simulation study, in which we verify that our FDII algorithm correctly detects each attack pattern.

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Section: Related Workmentioning

confidence: 99%

Cooperative Perception for Safe Control of Autonomous Vehicles under LiDAR Spoofing Attacks

Zhang¹,

Li²,

Cheng³

et al. 2023

Proceedings Inaugural International Symposium on Vehicle Security &Amp; Privacy

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“…With the continuing development of telecommunication systems and the advent of vehicle-to-everything communications, connected and autonomous vehicles (CAVs) systems have been widely applied [1][2][3]. CAVs systems, as illustrated in Figure 1, can help enhance road safety and improve traffic management.…”

Section: Introductionmentioning

confidence: 99%

Physics‐based wave propagation model assisted vehicle localisation in tunnels

Qin

Zhang

2023

IET Microwaves Antenna & Prop

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Connected and autonomous vehicles systems can provide a safe and convenient transportation solution through wireless communication between vehicles and roadside communication infrastructure. The vehicle's ability to establish its location plays an important role in such systems. This article presents an efficient localisation framework by combining physics‐based wireless propagation models and fingerprint localisation algorithms. To that end, a parabolic wave equation (PWE)‐based wireless channel simulator is utilised to obtain the direction of arrival (DOA) values that construct the fingerprint dataset. A detailed procedure on how to obtain the DOA information using PWE, as well as analysis and guidelines on the selection of the key parameters of the localisation framework are provided. Numerical results are compared with theoretical data in terms of accuracy, and the selection of parameters during the localisation process is investigated in actual road tunnel cases. Besides, various numbers of wireless access points and schemes of fingerprinting algorithms have been studied, showing the capability of the proposed approach to be employed for the pre‐design of wireless communication systems.

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