26 citations · 49 across the 8 of their papers we have counts for
8 papers
RCM-Fusion: Radar-Camera Multi-Level Fusion for 3D Object Detection
Jisong Kim, Minjae Seong, Geonho Bang +2
While LiDAR sensors have been successfully applied to 3D object detection, the affordability of radar and camera sensors has led to a growing interest in fusing radars and cameras…
SPADE: Sparse Pillar-based 3D Object Detection Accelerator for Autonomous Driving
Minjae Lee, Seongmin Park, Hyungmin Kim +6
3D object detection using point cloud (PC) data is essential for perception pipelines of autonomous driving, where efficient encoding is key to meeting stringent resource and laten…
CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception
Youngseok Kim, Juyeb Shin, Sanmin Kim +3
Autonomous driving requires an accurate and fast 3D perception system that includes 3D object detection, tracking, and segmentation. Although recent low-cost camera-based approache…
R-Pred: Two-Stage Motion Prediction Via Tube-Query Attention-Based Trajectory Refinement
Sehwan Choi, Jungho Kim, Junyong Yun +1
Predicting the future motion of dynamic agents is of paramount importance to ensuring safety and assessing risks in motion planning for autonomous robots. In this study, we propose…
Boosting Monocular 3D Object Detection with Object-Centric Auxiliary Depth Supervision
Youngseok Kim, Sanmin Kim, Sangmin Sim +2
Recent advances in monocular 3D detection leverage a depth estimation network explicitly as an intermediate stage of the 3D detection network. Depth map approaches yield more accur…
CRAFT: Camera-Radar 3D Object Detection with Spatio-Contextual Fusion Transformer
Youngseok Kim, Sanmin Kim, Jun Won Choi +1
Camera and radar sensors have significant advantages in cost, reliability, and maintenance compared to LiDAR. Existing fusion methods often fuse the outputs of single modalities at…
Diverse Multiple Trajectory Prediction Using a Two-stage Prediction Network Trained with Lane Loss
Sanmin Kim, Hyeongseok Jeon, Junwon Choi +1
Prior arts in the field of motion predictions for autonomous driving tend to focus on finding a trajectory that is close to the ground truth trajectory. Such problem formulations a…
SCALE-Net: Scalable Vehicle Trajectory Prediction Network under Random Number of Interacting Vehicles via Edge-enhanced Graph Convolutional Neural Network
Hyeongseok Jeon, Junwon Choi, Dongsuk Kum
Predicting the future trajectory of surrounding vehicles in a randomly varying traffic level is one of the most challenging problems in developing an autonomous vehicle. Since ther…