GNSS location verification in connected and autonomous vehicles using in-vehicle multimodal sensor data fusion

Abstract

Connected and Autonomous Vehicles (CAVs) rely on Global Navigation Satellite Systems (GNSS), e.g., the Global Positioning System (GPS), for the provision of accurate location information for various functionalities including Vehicle-to-Vehicle/Infrastructure (V2V/V2I) communication and self-navigation. However, GNSS- based location awareness is prone to spoofing attacks, where the attacker generates counterfeit satellite signals. This in turn poses a serious threat to the CAV, e.g., car, drone, etc., as well as the surrounding entities. Thus, this threat needs to be detected reliably and mitigated timely to prevent undesired consequences (e.g., damages, casualties, etc.). To this end, this work proposes a location verification solution that leverages in-vehicle sensor readings (e.g., accelerometer, etc.) and Signals of Opportunity (SoO), as an alternative source of location information. In particular, the multimodal sensor data with SoO location measurements are fused by means of a Kalman filter and the estimated fusion-based location is used to verify the location output of the GPS receiver. In case the GPS location deviates considerably from the fusion-based location, then a location spoofing attack is ascertained. Preliminary experimental results with real GPS and sensor data collected with a drone demonstrate the effectiveness of the proposed approach.