A Secure Sensor Fusion Framework for Connected and Automated Vehicles under Sensor Attacks

Abstract

By using various sensors to measure the surroundings and sharing local sensor information with the surrounding vehicles through wireless networks, connected and automated vehicles (CAVs) are expected to increase safety, efficiency, and capacity of our transportation systems. However, the increasing usage of sensors has also increased the vulnerability of CAVs to sensor faults and adversarial attacks. Anomalous sensor values resulting from malicious cyberattacks or faulty sensors may cause severe consequences or even fatalities. In this paper, we increase the resilience of CAVs to faults and attacks by using multiple sensors for measuring the same physical variable to create redundancy. We exploit this redundancy and propose a sensor fusion algorithm for providing a robust estimate of the correct sensor information with bounded errors independent of the attack signals, and for attack detection and isolation. The proposed sensor fusion framework is applicable to a large class of security-critical Cyber-Physical Systems (CPSs). To minimize the performance degradation resulting from the usage of estimation for control, we provide an H∞ controller for CACCequipped CAVs capable of stabilizing the closed-loop dynamics of each vehicle in the platoon while reducing the joint effect of estimation errors and communication channel noise on the tracking performance and string behavior of the vehicle platoon. Numerical examples are presented to illustrate the effectiveness of our methods.

Secure Cooperative Localization for Connected Automated Vehicles Based on Consensus

“…But in the meantime, this cooperation makes sensors or communication vulnerable to attacks [30]. Before fusing sensors from different sources, i.e, vehicles or infrastructure, the information should be inspected to detect attacks or other types of faults [31]. In [28], the velocity and the position of the CAVs in a platoon are estimated in an unknown input observer (UIO).…”

“…The scheme is able to generate an intermediate value only related to the attack for detecting the attack conveniently. In [31], given the multiple redundant sensors to measure the same physical variable of the CAV, the attacks are detected directly if there is a difference between the measurement from a specific sensor and the averaged measurement for all the sensors larger than a threshold. In [32], a drift attack caused by the GNSS spoofing is added to an optimizer as a variable to be solved.…”

“…Then, the attack is diagnosed based on the estimated value of the attack. For the different attack detection methods in [31], stochastic variables are generated, and then, one sample or multiple samples will be used to decide if an attack occurred [33]. The detection accuracy relies on the thresholds heavily if only one sample of the generated stochastic variable is used.…”

Millimeter-Wave Automotive Radar Spoofing

“…LiDAR/Camera sensor integration. Multi-sensor integration is another solution that has been proposed in literature as a potential countermeasure to radar object-spoofing attacks [28]. This works by measuring the same physical variables in the environment using different sensors and verifying the measurements between these.…”

Spoofing Attacks Against Vehicular FMCW Radar

“…I.e., when the mmWave sensor warns against impending collision, other sensors should agree. For example, in [29], the authors are proposing a sensor fusion framework that uses multiple sensors for mea-suring the same physical variable to create redundancy. Then it is used to estimate the real measurements and mitigate a physical level attack on sensor level.…”