Metraipolis › archive › dc_f2a2ef73cf48

MC-DeTra: Motion-Consistent Joint Object Detection and Socially-Aware Trajectory Forecasting in Bird's-Eye-View Images — arXiv:2609.11717

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.11717
License (as archived): CC0-1.0
Archived: 2026-10-16T06:35:47.036Z
SHA-256 of the archived text: 16a8a3d3d0c337c12f2cfd1f8d2768759bde34985186ad4de379465fd49fb370
Stable URL of this page: https://metraipolis.com/documento/dc_f2a2ef73cf48

How to cite

MC-DeTra: Motion-Consistent Joint Object Detection and Socially-Aware Trajectory Forecasting in Bird's-Eye-View Images — arXiv:2609.11717 — arXiv (en), https://arxiv.org/abs/2609.11717 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_f2a2ef73cf48 (Metraipolis archive dc_f2a2ef73cf48, sha256 16a8a3d3d0c337c12f2cfd1f8d2768759bde34985186ad4de379465fd49fb370)

BibTeX

@misc{dcf2a2ef73cf48,
  title = {MC-DeTra: Motion-Consistent Joint Object Detection and Socially-Aware Trajectory Forecasting in Bird's-Eye-View Images — arXiv:2609.11717},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.11717},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_f2a2ef73cf48; sha256 16a8a3d3d0c337c12f2cfd1f8d2768759bde34985186ad4de379465fd49fb370},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.11717. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Unified models for object detection and trajectory forecasting aim to merge perception and prediction for autonomous driving, refining actor trajectories directly over shared bird's-eye-view (BEV) images rasterized from LiDAR and high-definition maps. Their accuracy on dynamic, moving actors, however, remains the hardest part of the task, and the strongest such model, DeTra, has no public implementation. We contribute an openly released DeTra reimplementation with documented approximations, and on top of it MC-DeTra: a family of motion-consistency mechanisms that add supervision through two annotation-derived auxiliary signals -- each actor's observed past motion and the occupancy of the surrounding traffic that forms its social context -- and one inter-output consistency constraint that aligns an actor's predicted heading with its predicted direction of motion. Every proposed loss is train-only and inference-safe: it shapes the shared BEV representation during training and is removed at test time, adding no inference latency. On the Waymo Open Dataset, evaluated under a strict, detection-conditioned forecasting protocol, MC-DeTra improves dynamic, socially-situated trajectory forecasting while preserving or improving detection accuracy; a gradient-based loss-calibration analysis exposes how the auxiliary objectives compete at the shared backbone, and our ablation identifies which signals contribute most. We release code, configurations, and evaluation tooling at this https URL .

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-11 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.11717 — 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 2026-10-16T06:06:28.803Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-11?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).