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DriveMotion: A Large-Scale Multi-Source Benchmark for Driver Motion Sequence Modeling and Forecasting — arXiv:2609.08117

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.08117
License (as archived): CC0-1.0
Archived: 2026-10-16T13:46:53.009Z
SHA-256 of the archived text: 7a8d535d6c7e4f4e6cb76445ae9af37fd74346da990228b7aca3f1299716ec58
Stable URL of this page: https://metraipolis.com/documento/dc_85ef2febb19f

How to cite

DriveMotion: A Large-Scale Multi-Source Benchmark for Driver Motion Sequence Modeling and Forecasting — arXiv:2609.08117 — arXiv (en), https://arxiv.org/abs/2609.08117 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_85ef2febb19f (Metraipolis archive dc_85ef2febb19f, sha256 7a8d535d6c7e4f4e6cb76445ae9af37fd74346da990228b7aca3f1299716ec58)

BibTeX

@misc{dc85ef2febb19f,
  title = {DriveMotion: A Large-Scale Multi-Source Benchmark for Driver Motion Sequence Modeling and Forecasting — arXiv:2609.08117},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.08117},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_85ef2febb19f; sha256 7a8d535d6c7e4f4e6cb76445ae9af37fd74346da990228b7aca3f1299716ec58},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.08117. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Driver motion can provide cues to ongoing behavior, attention, and near-term driving intent. However, most existing driver-centric datasets focus on recognizing predefined driver behaviors from short video clips, while human motion forecasting benchmarks largely target motion outside the vehicle. We introduce DriveMotion, a multi-source benchmark for continuous driver motion forecasting. DriveMotion contains 393 hours of 133-keypoint motion sequences at 10 Hz from 360 drivers, integrating naturalistic driving data, curated public in-cabin videos, and the AIDE dataset into a unified representation with per-joint validity masks and synchronized driving context. Naturalistic driving contains long periods of limited body movement, making uniformly sampled evaluation dominated by persistence and less sensitive to brief but behaviorally meaningful motion. To address this, we use dynamics-anchored evaluation, placing forecasting windows around vehicle maneuvers identified offline from CAN signals without providing CAN to the model at inference. Arm motion in pre-maneuver windows is 3.4x greater than in route-matched stable-driving controls. On these anchored windows, learned models reduce forecasting error over persistence by up to 15%, while maneuver-enriched training improves forecast-derived Part-State F1 by 44% over the zero-motion reference. Training on the full multi-source corpus further reduces forecasting error on held-out web drivers by 38% compared with BATON-only training. DriveMotion provides identity-disjoint splits, fixed evaluation subsets, and reference implementations for reproducible evaluation of continuous driver motion forecasting. The dataset and benchmark are available at this https URL

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-09 in cs.HC).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Human-Computer Interaction (cs.HC).
Pagina del lavoro: https://arxiv.org/abs/2609.08117 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T13:28:43.167Z dalla pagina https://arxiv.org/catchup/cs.HC/2026-09-09?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).