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The accuracy and robustness of vehicle localization are critical forachieving safe and reliable high-level autonomy. Recent results show that GPSis vulnerable to spoofing attacks, which is one major threat to autonomousdriving. In this paper, a novel anomaly detection and mitigation method againstGPS attacks that utilizes onboard camera and high-precision maps is proposed toensure accurate vehicle localization. First, lateral direction localization indriving lanes is calculated by camera-based lane detection and map matchingrespectively. Then, a real-time detector for GPS spoofing attack is developedto evaluate the localization data. When the attack is detected, a multi-sourcefusion-based localization method using Unscented Kalman filter is derived tomitigate GPS attack and improve the localization accuracy. The proposed methodis validated in various scenarios in Carla simulator and open-source publicdataset to demonstrate its effectiveness in timely GPS attack detection anddata recovery.

GPS Attack Detection and Mitigation for Safe Autonomous Driving using Image and Map based Lateral Direction Localization