  The accuracy and robustness of vehicle localization are critical for
achieving safe and reliable high-level autonomy. Recent results show that GPS
is vulnerable to spoofing attacks, which is one major threat to autonomous
driving. In this paper, a novel anomaly detection and mitigation method against
GPS attacks that utilizes onboard camera and high-precision maps is proposed to
ensure accurate vehicle localization. First, lateral direction localization in
driving lanes is calculated by camera-based lane detection and map matching
respectively. Then, a real-time detector for GPS spoofing attack is developed
to evaluate the localization data. When the attack is detected, a multi-source
fusion-based localization method using Unscented Kalman filter is derived to
mitigate GPS attack and improve the localization accuracy. The proposed method
is validated in various scenarios in Carla simulator and open-source public
dataset to demonstrate its effectiveness in timely GPS attack detection and
data recovery.
