AoA-aided Kalman Filter to Mitigate False Data Injections Attacks in Vehicular Scenarios

In Autonomous Driving (AD), precise positioning is crucial for safe, efficient and cost-effective transportation. However, malicious cyber threats such as false data injection (FDI) attacks via Vehicle-to-Everything (V2X) links can significantly undermine these goals. FDI involves the insertion of incorrect data into the ego-vehicle or information system (IS), at specific points in time (Impulse FDI) or over specific intervals (Pulse FDI). To combat the specific threat of incorrect position data being shared by a Vehicle-of-Interest, our approach leverages collaboration between a set of trusted participants to perform positioning by exchanging Angle-of-Arrival (AoA) measurements over sidelinks paired with a Kalman Filter. Our method reduces localization errors caused by Impulse FDI to below 10m in 60% of cases, and the errors caused by Pulse FDI in 66.39% of cases.