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Fast-Slow Evolutionary Occupancy Prediction via Controlled Dynamics — arXiv:2611.02499

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.02499
License (as archived): CC0-1.0
Archived: 2026-11-03T11:42:23.841Z
SHA-256 of the archived text: f358c82746db2c923035814e786141658c25cd78b8809b5cb815204163473d9b
Stable URL of this page: https://metraipolis.com/documento/dc_3cdacaee4df8

How to cite

Fast-Slow Evolutionary Occupancy Prediction via Controlled Dynamics — arXiv:2611.02499 — arXiv (en), https://arxiv.org/abs/2611.02499 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_3cdacaee4df8 (Metraipolis archive dc_3cdacaee4df8, sha256 f358c82746db2c923035814e786141658c25cd78b8809b5cb815204163473d9b)

BibTeX

@misc{dc3cdacaee4df8,
  title = {Fast-Slow Evolutionary Occupancy Prediction via Controlled Dynamics — arXiv:2611.02499},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.02499},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_3cdacaee4df8; sha256 f358c82746db2c923035814e786141658c25cd78b8809b5cb815204163473d9b},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.02499. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

3D semantic occupancy prediction provides dense geometric and semantic scene representations for autonomous driving, where both high accuracy and low response latency are crucial for safe downstream forecasting and planning. Existing methods usually exhibit different accuracy-latency characteristics. Performance-oriented models provide stronger geometric and semantic predictions, while deployment-friendly models provide faster responses. This motivates EvoOcc, a fast-slow evolutionary occupancy prediction framework that jointly exploits their complementary strengths to improve the accuracy-latency balance without changing the upstream architectures. EvoOcc models evolutionary occupancy prediction as a continuous time controlled dynamical system, enabling flexible adaptation to variable slow system delays and irregular fast system arrivals. The framework further decomposes the evolution process into dual state evolution where ego state evolution compensates for the deterministic coordinate shifts and scene state evolution models the genuine geometric and semantic scene changes. Extensive experiments on three different fast-slow system pairs and irregular prediction arrivals show that EvoOcc consistently improves over fast system baselines while maintaining much lower latency than the slow system, indicating a favorable accuracy-latency balance and stable behavior under practical inference situations.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.CV, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2611.02499 — 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 2026-11-03T11:06:37.558Z dalla pagina https://arxiv.org/list/cs.CV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).