Phantom Radar Object Injection in Automotive Automatic Emergency Braking: Attack Quantification, Two-Layer Software Countermeasure, and Dual ISO/SAE 21434 and ISO 21448 Compliance Evidence

Automotive radar systems do not authenticate the signals they receive, allowing an external transmitter to create phantom objects that can trigger automatic emergency braking (AEB). This paper quantifies the threat using real radar measurements from the public RadarScenes dataset and evaluates a software countermeasure that requires no hardware modification. Computer-generated phantom radar returns were added to 200 motorway sequences and adjusted so the OS-CFAR detector accepted them as real objects. In the undefended system, radar detections were tracked as obstacles and braking was initiated when an object appeared within 30 m, resulting in unnecessary braking in 87.0 percent of attacked sequences. The proposed two-layer filter combines kinematic plausibility gating with camera-radar cross-validation. Layer 1 checks track history and physical movement over three consecutive frames, while Layer 2 reduces confidence when a clear-weather radar detection lacks camera confirmation. With both layers active, false braking fell to 3.5 percent, with no genuine detections missed across 200 genuine sequences. SHAP analysis of a Random Forest classifier identified kinematic implausibility as responsible for 62.3 percent of correct suppressions and produced a machine-readable evidence record to support cybersecurity documentation under ISO/SAE 21434 and safety documentation under ISO 21448. These results apply to an offline replay experiment using synthetic injections. Validation with real attack hardware, synchronized camera data, and vehicle testing remains future work.