4 citations · 5 across the 8 of their papers we have counts for
8 papers
BEV-Denoise: Learning Intrinsic Noise for Accurate Bird's-Eye-View Semantic Segmentation
Dooseop Choi, Kyounghwan An, Kyoung-Wook Min
In this paper, we present a framework dubbed \textbf{BEV-Denoise} that estimates and removes intrinsic noise from learned Bird's-Eye-View (BEV) features to achieve accurate BEV sem…
CycleBEV: Regularizing View Transformation Networks via View Cycle Consistency for Bird's-Eye-View Semantic Segmentation
Jeongbin Hong, Dooseop Choi, Taeg-Hyun An +2
Transforming image features from perspective view (PV) space to bird's-eye-view (BEV) space remains challenging in autonomous driving due to depth ambiguity and occlusion. Although…
LANet: A Lane Boundaries-Aware Approach For Robust Trajectory Prediction
Muhammad Atta ur Rahman, Dooseop Choi, KyoungWook Min
Accurate motion forecasting is critical for safe and efficient autonomous driving, enabling vehicles to predict future trajectories and make informed decisions in complex traffic s…
Progressive Query Refinement Framework for Bird's-Eye-View Semantic Segmentation from Surrounding Images
Dooseop Choi, Jungyu Kang, Taeghyun An +2
Expressing images with Multi-Resolution (MR) features has been widely adopted in many computer vision tasks. In this paper, we introduce the MR concept into Bird's-Eye-View (BEV) s…
Hierarchical Latent Structure for Multi-Modal Vehicle Trajectory Forecasting
Dooseop Choi, KyoungWook Min
Variational autoencoder (VAE) has widely been utilized for modeling data distributions because it is theoretically elegant, easy to train, and has nice manifold representations. Ho…
PathGAN: Local Path Planning with Attentive Generative Adversarial Networks
Dooseop Choi, Seung-jun Han, Kyoungwook Min +1
To achieve autonomous driving without high-definition maps, we present a model capable of generating multiple plausible paths from egocentric images for autonomous vehicles. Our ge…
Regularizing Neural Networks for Future Trajectory Prediction via Inverse Reinforcement Learning Framework
Dooseop Choi, Kyoungwook Min, Jeongdan Choi
Predicting distant future trajectories of agents in a dynamic scene is not an easy problem because the future trajectory of an agent is affected by not only his/her past trajectory…
Driving Experience Transfer Method for End-to-End Control of Self-Driving Cars
Dooseop Choi, Taeg-Hyun An, Kyounghwan Ahn +1
In this paper, we present a transfer learning method for the end-to-end control of self-driving cars, which enables a convolutional neural network (CNN) trained on a source domain…