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
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
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
RLG-TPV introduces a multimodal Tri-Perspective View framework that fuses camera, radar, and training‑time LiDAR data for 3D object detection. It uses radar and LiDAR to guide a ray‑deformable attention lift, refining depth distributions and providing geometric supervision for side and front planes, while radar cross‑section awareness spreads evidence spatially. On nuScenes, the method attains 0.4981 mAP and 0.5959 NDS, improving orientation and velocity accuracy by about 32 % and 31 % over the CRN baseline.
By Ahmet Mete Dokgoz, A. Enes Doruk, Hasan F. Ates
arXiv:2609.18542v1 Announce Type: new
Abstract: 4D radar has emerged as a promising next-generation sensor for improving the robustness of autonomous driving perception systems because of its stable...
By Woo-Jin Jung, Dong-Hee Paek, Jeong-Su Park, 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.