TEMPEST: Temporal Embeddings for Scalable Driver Identification via Angular Margin Learning

Scalable driver identification requires embedding models that maintain discriminative performance as fleet size grows, yet existing triplet-loss formulations degrade rapidly with driver pool size and overfit to session-specific patterns under rigorous temporal evaluation.