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New TEMPEST model offers scalable driver identification with high accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed TEMPEST, a new Temporal Convolutional Network embedding model that uses an additive angular margin loss (ArcFace) for scalable driver identification. This model maps 60-second multimodal driving windows into 96-dimensional embeddings, allowing for dynamic enrollment without retraining. TEMPEST demonstrated strong performance on a 45-driver dataset, achieving 91.71% Rank-1 accuracy and significantly outperforming existing methods, particularly in maintaining performance as the driver pool size increases. AI

IMPACT This research could lead to more robust and scalable biometric identification systems for vehicles and other applications.

RANK_REASON The cluster contains an academic paper detailing a new model and its performance on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New TEMPEST model offers scalable driver identification with high accuracy

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The cluster contains an academic paper detailing a new model and its performance on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Kyle Musgrove, Dylan B. Lewis, Sarah Powers, Emma J. Reid, Hector Santos-Villalobos ·

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

    arXiv:2610.06855v1 Announce Type: new Abstract: 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 patt…

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