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4D Radar Perception Algorithms for Autonomous Driving: A Review — arXiv:2609.19216

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.19216
License (as archived): CC0-1.0
Archived: 2026-10-10T05:08:08.455Z
SHA-256 of the archived text: dd0f4b3fe57ee4e879c4028f0f03f9aeedd22a429f0a924fa2bdc7389c3128a8
Stable URL of this page: https://metraipolis.com/documento/dc_bc590d448353

How to cite

4D Radar Perception Algorithms for Autonomous Driving: A Review — arXiv:2609.19216 — arXiv (en), https://arxiv.org/abs/2609.19216 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bc590d448353 (Metraipolis archive dc_bc590d448353, sha256 dd0f4b3fe57ee4e879c4028f0f03f9aeedd22a429f0a924fa2bdc7389c3128a8)

BibTeX

@misc{dcbc590d448353,
  title = {4D Radar Perception Algorithms for Autonomous Driving: A Review — arXiv:2609.19216},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.19216},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bc590d448353; sha256 dd0f4b3fe57ee4e879c4028f0f03f9aeedd22a429f0a924fa2bdc7389c3128a8},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.19216. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Research on 4D millimeter-wave radar perception algorithms has flourished in recent years, extending from signal processing and object detection to semantic segmentation, motion estimation, occupancy prediction, and dynamic scene reconstruction. This review organizes the field according to the evolution of perception tasks and algorithms. It first introduces radar fundamentals, data representations, and quality-enhancement methods, and then reviews object-level perception, motion and localization, local and dense spatial perception, and dynamic scene understanding. Across these directions, we compare radar-only learning, multimodal fusion, and cross-modal supervision and knowledge distillation. Particular attention is paid to how elevation, Doppler measurements, and radar physical priors are exploited across tasks. We further summarize the task coverage, input data, annotations, and evaluation protocols of existing datasets, clarifying the empirical support for different research directions. Finally, we discuss the common challenges and future directions of 4D radar perception for autonomous driving. This review provides a task-oriented perspective on the transition from sparse object perception to dynamic spatial understanding.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-18 in cs.CV).
Categorie arXiv: Robotics (cs.RO) ; Computer Vision and Pattern Recognition (cs.CV); Signal Processing (eess.SP).
Pagina del lavoro: https://arxiv.org/abs/2609.19216 — 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 2026-10-10T04:26:09.637Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-18?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).