TEMPEST: Temporal Embeddings for Scalable Driver Identification via Angular Margin Learning
TEMPEST uses Temporal Convolutional Networks and ArcFace loss to improve driver identification scalability.
Existing triplet-loss models often struggle with large driver pools and overfitting. TEMPEST addresses this by using an additive angular margin loss to enforce global class-level separation in a normalized angular space. The model processes 60-second multimodal driving windows into compact 96-dimensional embeddings.