Angular Exploitation of FMCW Radars in Autonomous Driving

Frequency modulated continuous wave(FMCW) radars are vital for advanced driver assistance systems(ADAS) but remain vulnerable to spoofing attacks that produce fake obstacles and cause false braking, while current research focuses on fixed-distance targets and it cannot generate false targets at different angles. In this work, we present GHOSTRADAR, a novel attack framework that injects signals into a victim radar to create fake objects at specific angles and distances. Using a ray-tracing mmWave radar simulator, we show GHOSTRADAR achieves an average distance error of 0.45 m and angular error of 2.67°, demonstrating precise spoofing and revealing radar vulnerabilities.