---
format: "aidr-story-markdown/v1"
id: "6f2df0bab3eeb195ad4d94aff4543038d514aef33ce7ac5c02cb00f99148bc17"
canonical_url: "https://aidr.today/6f2df0ba?lang=en"
title: "TEMPEST: Temporal Embeddings for Scalable Driver Identification via Angular Margin Learning"
lang: "en"
requested_lang: "en"
available_langs: ["en","vi"]
translation_fallback: null
fallback_fields: []
published_at: "2026-10-07T04:00:00.000Z"
category: "Research"
topics: ["inference"]
source_urls: ["https://arxiv.org/abs/2610.06855"]
summary: "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."
---

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

> [Open the canonical story](<https://aidr.today/6f2df0ba?lang=en>)

**Published:** 2026-10-07T04:00:00.000Z
**Category:** Research
**Topics:** inference

## Summary

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\.

## Sources

- [Story source](<https://arxiv.org/abs/2610.06855>)