26 citations · 88 across the 18 of their papers we have counts for
19 papers
RAF-VLA: Representation Alignment with the Future for End-to-End Autonomous Driving
Dogun Kim, Yongjae Lee, Joonhee Lim +4
Recent Vision-Language-Action (VLA) models for autonomous driving have incorporated world modeling by predicting future driving scenes alongside driving actions, demonstrating stro…
MapTCL: Temporal Consistency Learning via Bidirectional Alignment for Vectorized HD Map Construction
Hyeonseo Kim, Juyeb Shin, Hyeonjun Jeong +2
Constructing reliable online HD maps remains challenging in dynamic urban environments due to moving objects and occlusions. While recent works employ feature-level temporal fusion…
PlanRL: A Trajectory Planning Architecture for Reinforcement Learning-based Driving Experts
Joonhee Lim, Yongjae Lee, Jangho Shin +1
Reinforcement learning (RL) has become a prominent framework for developing driving experts in autonomous vehicles. However, most existing RL-based experts are designed to output d…
Class-Distribution Guided Active Learning for 3D Occupancy Prediction in Autonomous Driving
Wonjune Kim, In-Jae Lee, Sihwan Hwang +2
3D occupancy prediction provides dense spatial understanding critical for safe autonomous driving. However, this task suffers from a severe class imbalance due to its volumetric re…
REOcc: Camera-Radar Fusion with Radar Feature Enrichment for 3D Occupancy Prediction
Chaehee Song, Sanmin Kim, Hyeonjun Jeong +3
Vision-based 3D occupancy prediction has made significant advancements, but its reliance on cameras alone struggles in challenging environments. This limitation has driven the adop…
CRAB: Camera-Radar Fusion for Reducing Depth Ambiguity in Backward Projection based View Transformation
In-Jae Lee, Sihwan Hwang, Youngseok Kim +3
Recently, camera-radar fusion-based 3D object detection methods in bird's eye view (BEV) have gained attention due to the complementary characteristics and cost-effectiveness of th…
LabelDistill: Label-guided Cross-modal Knowledge Distillation for Camera-based 3D Object Detection
Sanmin Kim, Youngseok Kim, Sihwan Hwang +2
Recent advancements in camera-based 3D object detection have introduced cross-modal knowledge distillation to bridge the performance gap with LiDAR 3D detectors, leveraging the pre…
RadarDistill: Boosting Radar-based Object Detection Performance via Knowledge Distillation from LiDAR Features
Geonho Bang, Kwangjin Choi, Jisong Kim +2
The inherent noisy and sparse characteristics of radar data pose challenges in finding effective representations for 3D object detection. In this paper, we propose RadarDistill, a…
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…
Predict to Detect: Prediction-guided 3D Object Detection using Sequential Images
Sanmin Kim, Youngseok Kim, In-Jae Lee +1
Recent camera-based 3D object detection methods have introduced sequential frames to improve the detection performance hoping that multiple frames would mitigate the large depth es…
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…
InstaGraM: Instance-level Graph Modeling for Vectorized HD Map Learning
Juyeb Shin, Hyeonjun Jeong, Francois Rameau +1
For scalable autonomous driving, a robust map-based localization system, independent of GPS, is fundamental. To achieve such map-based localization, online high-definition (HD) map…
3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object Detection
Yecheol Kim, Konyul Park, Minwook Kim +2
Fusing data from cameras and LiDAR sensors is an essential technique to achieve robust 3D object detection. One key challenge in camera-LiDAR fusion involves mitigating the large d…
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…
Joint 3D Object Detection and Tracking Using Spatio-Temporal Representation of Camera Image and LiDAR Point Clouds
Junho Koh, Jaekyum Kim, Jinhyuk Yoo +3
In this paper, we propose a new joint object detection and tracking (JoDT) framework for 3D object detection and tracking based on camera and LiDAR sensors. The proposed method, re…
LaPred: Lane-Aware Prediction of Multi-Modal Future Trajectories of Dynamic Agents
ByeoungDo Kim, Seong Hyeon Park, Seokhwan Lee +5
In this paper, we address the problem of predicting the future motion of a dynamic agent (called a target agent) given its current and past states as well as the information on its…
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…