31 citations · 32 across the 14 of their papers we have counts for
20 papers
RadarGSOcc: Doppler-Aware 3D Semantic Occupancy Prediction Using 4D Radar Tensor with Geometric-Semantic Feature Extraction
Christofel Rio Goenawan, Dong-Hee Paek, Seung-Hyun Kong
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 weathe…
STAR: Scene- and Task-Aware 4D Radar Preprocessing Towards End-to-End Cognitive Radar
Seung-Hyun Song, Dong-Hee Paek, Seung-Hyun Kong
Four-dimensional (4D) Radar has emerged as a key sensor for environmental perception, providing range, azimuth, elevation, and Doppler measurements while remaining robust to illumi…
Stabilizing Trajectory Outputs in End-to-End Autonomous Driving via SC-IMM Based Teacher Signals
Siewoo Kim, Seung-Hyun Kong
End-to-End autonomous driving models commonly predict future waypoints from sensor inputs and convert them into vehicle control commands through a downstream controller. However, c…
Vehicle Trajectory Prediction via Neural Fusion of Multiple EKF-Based Trajectory Candidates
Seong-Jun Kim, Seung-Hyun Kong
Predicting the future trajectories of surrounding vehicles in autonomous driving is important for collision risk assessment and safe ego-vehicle path planning. Conventional neural…
IMM-based Multiple Object Tracking using a State Prediction Neural Network
Chan-Bin Lim, Dong-Hee Paek, Seung-Hyun Kong
Object tracking is essential for autonomous vehicles to avoid obstacles and plan routes. Radar maintains detection performance even in adverse weather and can measure relative velo…
Frequency Estimation Based on SNR-adaptive Frequency Estimator Under Wide SNR Range
Hee-Yang Jung, Dong-Hee Paek, Woo-Jin Jung +1
Frequency estimation is the problem of estimating individual tone frequencies from noisy multi-tone sinusoidal signals. Existing frequency estimation methods have difficulty accura…
Stereo 4D Radar for 3D Object Detection: Integrating Geometric Alignment and Absolute Velocity Estimation
Seung-Hyun Song, Dong-Hee Paek, Woong-Chan Byun +1
Four-dimensional (4D) Radar is a powerful sensing modality capable of detecting surrounding three-dimensional (3D) objects under diverse weather conditions and providing Doppler-ba…
DNC-IMM: Early Lane-Change Intention Recognition via Neural Calibration Based on Driving Context Information
Woong-Chan Byun, Seung-Hyun Kong
Early recognition of lane-change intention is essential for proactive decision-making in autonomous driving and advanced driver assistance systems. This paper proposes a Dual Neura…
Efficient On-Chip Implementation of 4D Radar-Based 3D Object Detection on Hailo-8L
Woong-Chan Byun, Dong-Hee Paek, Seung-Hyun Song +1
4D radar has attracted attention in autonomous driving due to its ability to enable robust 3D object detection even under adverse weather conditions. To practically deploy such tec…
Availability-aware Sensor Fusion via Unified Canonical Space
Dong-Hee Paek, Seung-Hyun Kong
Sensor fusion of camera, LiDAR, and 4-dimensional (4D) Radar has brought a significant performance improvement in autonomous driving. However, there still exist fundamental challen…
L2RDaS: Synthesizing 4D Radar Tensors for Model Generalization via Dataset Expansion
Woo-Jin Jung, Dong-Hee Paek, Seung-Hyun Kong
4-dimensional (4D) radar is increasingly adopted in autonomous driving for perception tasks, owing to its robustness under adverse weather conditions. To better utilize the spatial…
4DR P2T: 4D Radar Tensor Synthesis with Point Clouds
Woo-Jin Jung, Dong-Hee Paek, Seung-Hyun Kong
In four-dimensional (4D) Radar-based point cloud generation, clutter removal is commonly performed using the constant false alarm rate (CFAR) algorithm. However, CFAR may not fully…
Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar
Dong-In Kim, Dong-Hee Paek, Seung-Hyun Song +1
Accurate 3D multi-object tracking (MOT) is vital for autonomous vehicles, yet LiDAR and camera-based methods degrade in adverse weather. Meanwhile, Radar-based solutions remain rob…
Enhanced 3D Object Detection via Diverse Feature Representations of 4D Radar Tensor
Seung-Hyun Song, Dong-Hee Paek, Minh-Quan Dao +2
Recent advances in automotive four-dimensional (4D) Radar have enabled access to raw 4D Radar Tensor (4DRT), offering richer spatial and Doppler information than conventional point…
SpikingRTNH: Spiking Neural Network for 4D Radar Object Detection
Dong-Hee Paek, Seung-Hyun Kong
Recently, 4D Radar has emerged as a crucial sensor for 3D object detection in autonomous vehicles, offering both stable perception in adverse weather and high-density point clouds…
Open-Source Autonomous Driving Software Platforms: Comparison of Autoware and Apollo
Hee-Yang Jung, Dong-Hee Paek, Seung-Hyun Kong
Full-stack autonomous driving system spans diverse technological domains-including perception, planning, and control-that each require in-depth research. Moreover, validating such…
Enhancing Performance of Point Cloud Completion Networks with Consistency Loss
Kevin Tirta Wijaya, Christofel Rio Goenawan, Seung-Hyun Kong
Point cloud completion networks are conventionally trained to minimize the disparities between the completed point cloud and the ground-truth counterpart. However, an incomplete ob…
Efficient 4D Radar Data Auto-labeling Method using LiDAR-based Object Detection Network
Min-Hyeok Sun, Dong-Hee Paek, Seung-Hyun Song +1
Focusing on the strength of 4D (4-Dimensional) radar, research about robust 3D object detection networks in adverse weather conditions has gained attention. To train such networks,…
RTNH+: Enhanced 4D Radar Object Detection Network using Combined CFAR-based Two-level Preprocessing and Vertical Encoding
Seung-Hyun Kong, Dong-Hee Paek, Sangjae Cho
Four-dimensional (4D) Radar is a useful sensor for 3D object detection and the relative radial speed estimation of surrounding objects under various weather conditions. However, si…
K-Lane: Lidar Lane Dataset and Benchmark for Urban Roads and Highways
Donghee Paek, Seung-Hyun Kong, Kevin Tirta Wijaya
Lane detection is a critical function for autonomous driving. With the recent development of deep learning and the publication of camera lane datasets and benchmarks, camera lane d…