🧪 Test?View on arXiv
TEMPEST: Temporal Embeddings for Scalable Driver Identification via Angular Margin Learning
Not provided in the abstract
temporal embeddingsdriver identificationangular margin learningbiometric identification
2610.06855
Builder Relevance
1h ago80%
Abstract
TEMPEST introduces a novel embedding model for scalable driver identification that maintains performance as fleet size increases.
Reality Card
Core Claim
TEMPEST achieves 91.71% Rank-1 accuracy in driver identification while degrading only 4.3 pp when increasing the driver pool from 10 to 45 drivers.
Method / Result
Outperforms the best classical model by 17.9 pp and the strongest triplet-loss baseline by 58.4 pp.
Limitations
The abstract does not specify limitations or reproducibility concerns.
Paper to code
Verified implementation resources so builders can test the paper’s claims instead of stopping at the abstract.
No verified implementation link has been attached yet. AIBuzzHub will keep this panel separate from unverified search results.