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

Researchers introduced TEMPEST, a Temporal Convolutional Network embedding model trained with an additive angular margin loss that maps 60-second multimodal driving windows to compact 96-dimensional embeddings for scalable driver identification. Under rigorous temporal evaluation on a 45-driver dataset, TEMPEST achieved 91.71% Rank-1 accuracy, outperforming the best classical model by 17.9 pp and the strongest triplet-loss baseline by 58.4 pp.