Prioritizing Safety Recalls Using AI-Driven Risk Models on Connected Vehicle Operating Data

Abstract

Connected vehicle technologies enable unprecedented opportunities for differentiating safety recall urgency through AI-driven risk assessment frameworks that leverage real-time operational telemetry and historical maintenance records. Traditional recall campaigns treat all affected vehicles uniformly despite substantial variations in actual failure probability based on usage patterns, operating conditions, and component stress levels. The proposed framework combines gradient boosted tree models with temporal neural networks to generate calibrated risk scores that identify vehicles most likely to experience imminent safety-critical failures. Telemetry streams, including engine load distributions, thermal exposure patterns, braking system stress indicators, and diagnostic trouble codes, provide input features that capture both chronic degradation and acute anomaly signals. Risk-based prioritization enables service centers to schedule the highest-risk vehicles for expedited repairs while maintaining standard timelines for lower-risk units, optimizing allocation of constrained dealer resources, including service capacity and replacement parts inventory. Operational deployment through cloud-native streaming architectures processes high-velocity telemetry at scale with real-time scoring capabilities integrated into dealer management systems and customer notification channels. Explainability mechanisms using feature attribution methods provide a transparent rationale for individual risk classifications, supporting regulatory compliance and customer communication requirements. Empirical validation demonstrates substantial reductions in post-notification field failures when the highest-risk vehicles receive prioritized outreach compared to uniform notification strategies. Customer satisfaction improvements emerge from proactive communication that demonstrates manufacturer concern through personalized risk assessment and convenient scheduling options. Legal risk mitigation benefits arise from documented data-driven prioritization that strengthens the defensibility of recall processes in regulatory reviews and litigation contexts. The framework aligns automotive manufacturers to transform connected vehicle data into operational safety intelligence that enhances the outcomes on public safety, customer experience, operational efficiency, and legal exposure facets.