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By using various sensors to measure the surroundings and sharing local sensorinformation with the surrounding vehicles through wireless networks, connectedand automated vehicles (CAVs) are expected to increase safety, efficiency, andcapacity of our transportation systems. However, the increasing usage ofsensors has also increased the vulnerability of CAVs to sensor faults andadversarial attacks. Anomalous sensor values resulting from maliciouscyberattacks or faulty sensors may cause severe consequences or evenfatalities. In this paper, we increase the resilience of CAVs to faults andattacks by using multiple sensors for measuring the same physical variable tocreate redundancy. We exploit this redundancy and propose a sensor fusionalgorithm for providing a robust estimate of the correct sensor informationwith bounded errors independent of the attack signals, and for attack detectionand isolation. The proposed sensor fusion framework is applicable to a largeclass of security-critical Cyber-Physical Systems (CPSs). To minimize theperformance degradation resulting from the usage of estimation for control, weprovide an $H_{\infty}$ controller for CACC-equipped CAVs capable ofstabilizing the closed-loop dynamics of each vehicle in the platoon whilereducing the joint effect of estimation errors and communication channel noiseon the tracking performance and string behavior of the vehicle platoon.Numerical examples are presented to illustrate the effectiveness of ourmethods.

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