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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. We introduce TEMPEST, a Tem
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