The paper presents a stereo 4D Radar framework for 3D object detection that uses geometric disparity between left and right radars to estimate absolute velocity and fuse complementary features. It addresses clutter, ghost reflections, and sparse data issues inherent in raw 4D Radar signals. Experiments on an in‑house dataset show significant gains, improving AP 3D by 8.82 points and AP BEV by 9.0 points over mono‑radar baselines.
By Seung-Hyun Song, Dong-Hee Paek, Woong-Chan Byun, Seung-Hyun Kong
The paper presents a stereo 4D Radar-based framework for 3D object detection that uses the geometric disparity between left and right radars to estimate absolute velocity and fuse complementary features. It addresses challenges such as clutter, ghost reflections, and sparse data caused by preprocessing, and improves motion state estimation beyond the radial Doppler component. Experiments on an in‑house stereo 4D Radar dataset show significant gains of 8.82 points in AP 3D and 9.0 points in AP BEV over mono‑radar baselines.
arXiv:2609.24151v1 Announce Type: new
Abstract: Four-dimensional (4D) Radar has emerged as a key sensor for environmental perception, providing range, azimuth, elevation, and Doppler measurements whi...
By Seung-Hyun Song, Dong-Hee Paek, Seung-Hyun Kong
arXiv:2602. 11554v3 Announce Type: replace-cross Abstract: How far can 3D object detection go using 4D radar alone?
By Yichun Xiao, Runwei Guan, Jin Jin, Fangqiang Ding
arXiv:2512.14235v2 Announce Type: replace
Abstract: Automotive radar has shown promising developments in environment perception due to its cost-effectiveness and robustness in adverse weather conditi...
By Jimmie Kwok, Holger Caesar, Andras Palffy
The review surveys 4D millimeter‑wave radar perception algorithms for autonomous driving, covering signal processing, object detection, semantic segmentation, motion estimation, occupancy prediction, and dynamic scene reconstruction. It organizes the field by perception tasks, discusses radar fundamentals, data representations, and quality‑enhancement methods, and compares radar‑only learning, multimodal fusion, and cross‑modal supervision. The paper also summarizes datasets, annotations, evaluation protocols, and outlines common challenges and future research directions.
By Xumin Wu, Jun Zhou, Jilin Mei, Chen Min, Yu Hu
arXiv:2606. 09634v1 Announce Type: cross Abstract: 3D object detection is the backbone of perception for automated vehicles (AV) and broader intelligent transportation systems applications.
By Debojyoti Biswas, Xianbiao Hu
3D object detection is the backbone of perception for automated vehicles (AV) and broader intelligent transportation systems applications. Long-range detection is challenging because sensing evidence is sparse; yet this ``long-range'' scenario is routine in traffic.
arXiv:2607. 09629v1 Announce Type: cross Abstract: Reliable autonomous driving requires full-scene perception that couples foreground objects with dense semantic layout.
By Xiaokai Bai, Lianqing Zheng, Runwei Guan, Songkai Wang, Siyuan Cao, Hui-liang Shen
DyRAD introduces a novel radar novel‑view synthesis framework that models dynamic driving scenes by separating static background reflectors from motion‑tracked dynamic point reflectors, enabling the rendering of full range‑azimuth‑Doppler (RAD) tensors. The method derives reflector velocities from object tracks, projects them onto the line of sight, and uses a fixed analytic point‑spread function to avoid embedding sensor‑induced spread into the scene representation. This design allows accurate scene reconstruction and zero‑shot transfer to different radar configurations, achieving a 90.7% recovery of radar detections on the RADIal dataset compared to 26.9% for the best baseline.
By Merav Keidar, Tomer Borreda, Rajalakshmi Nandakumar, Or Litany
GRADE is a method for estimating high‑fidelity metric depth from a single radar frame, even when visual sensors fail due to smoke, fog, or darkness. It first converts raw 4D radar spectra into coarse depth, then uses a latent diffusion model conditioned on this estimate to recover fine structural detail. A pixel‑space adapter incorporates any available camera cues and is trained across clear, smoke‑degraded, and occluded inputs, allowing the output to rely more on radar as visibility worsens. On a dataset of ~95K frames from 12 buildings with real smoke, GRADE achieves an MAE of 0.303 m in clear scenes and 0.313 m under smoke, outperforming existing baselines.
By Bin Zhao, Patrick Chiou, Nakul Garg
The paper evaluates infrastructure‑based radar for highway traffic monitoring using the newly introduced DRaT dataset, which pairs radar data with drone‑derived ground truth. It reports that radar achieves 78 % precision and 57 % recall for vehicle detection, tracks vehicles with an IDF1 score of 0.699, and estimates space‑mean speed with less than 4 % error while underestimating density and volume by about 23 %. The study also highlights deployment considerations and releases the dataset for reproducible research.
By Tianheng Zhu, Woei-chyi Chang, Alamss Riaz, Sogand Hasanzadeh, Yiheng Feng