3D semantic occupancy prediction is crucial for autonomous driving, but camera-based methods degrade in low-light conditions and LiDAR-based methods are sensitive to adverse weather. 4D radar is more robust, yet its sparsity and the high cost of radar tensor processing limit its use. We propose Radar Geometric-Semantic...
Christofel Rio Goenawan, Dong-Hee Paek, Seung-Hyun Kong· 0 citations
A preprocessing framework for 4D-radar-based 3D object detection that extracts point clouds from radar tensors while preserving object-shape information and suppressing noise and false alarms is proposed.
Woo-Jin Jung, Dong-Hee Paek, Jeongsu Park et al.· 0 citations
Four-dimensional (4D) Radar has emerged as a key sensor for environmental perception, providing range, azimuth, elevation, and Doppler measurements while remaining robust to illumination changes and adverse weather conditions. However, conventional Radar preprocessing methods, such as constant false alarm rate (CFAR) d...
A data-driven state PRedictor-based Interacting Multiple Model tracking method (PR-IMM) that improves nonlinear object-motion representation while preserving the stability and interpretability of physics-based motion models.
A stereo 4D Radar-based 3D object detection framework that exploits the geometric disparity between left and right Radars to estimate the absolute velocity of objects and achieve more robust perception through the fusion of their complementary features is introduced.
Seung-Hyun Song, Dong-Hee Paek, Woong-Chan Byun et al.· 0 citations
SAFE consists of a time-frequency image neural network, which enhances weak tone frequency components at low SNR, and an SNR-based frequency selector, which selects an appropriate frequency estimator according to the SNR of the estimated tone frequencies, enabling SAFE to achieve robustness at low SNR while preserving...
Hee-Yang Jung, Dong-Hee Paek, Woo-Jin Jung et al.· 0 citations
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