Early detection of emerging vehicle safety defects is essential to reducing preventable crash injuries and supporting timely, evidence-based safety interventions. Current surveillance relied predominantly on consumer complaint mining, which suffers from self-reporting bias and substantial lag behind real-world harm. Police-reported crash data offer an objective outcome-side signal, but nationally sampled datasets such as the NHTSA Crash Report Sampling System (CRSS) exhibit pervasive exogenous distribution shifts—from sampling-frame redesigns, coding revisions, and fleet turnover—that are easily mistaken for defect-driven anomalies. This paper presents SafeSignal, a distribution-shift-aware framework that detects emerging defect signatures at the make–model–model-year cohort level from CRSS data (2016–2024, ≈430,000 sampled crashes). SafeSignal learns survey-weight-calibrated representations of crash configurations and outcomes, applies a density-ratio-based decomposition to separate population-level exogenous drift from cohort-specific residual anomalies, and converts anomalies into alarm scores with controlled false-discovery rates via a conformally calibrated Bayesian scan statistic. Retrospective validation against 63 major NHTSA recalls shows that SafeSignal detects 71% of recalls at a 5% false-alarm budget—14 to 26 percentage points above complaint-based and shift-agnostic baselines—and, for the recalls it detects, raises alarms a median of 7.2 months before announcement. These findings demonstrate the potential of distribution-shift-aware crash analytics to strengthen national vehicle-defect surveillance, complement complaint-based monitoring, and help safety agencies identify emerging risks before they result in broader public harm.