2 citations · 4 across the 9 of their papers we have counts for
10 papers
SMOL-MapSeg: Show Me One Label as prompt
Yunshuang Yuan, Frank Thiemann, Thorsten Dahms +1
Historical maps offer valuable insights into changes on Earth's surface but pose challenges for modern segmentation models due to inconsistent visual styles and symbols. While deep…
Leveraging LLMs and attention-mechanism for automatic annotation of historical maps
Yunshuang Yuan, Monika Sester
Historical maps are essential resources that provide insights into the geographical landscapes of the past. They serve as valuable tools for researchers across disciplines such as…
SparseAlign: A Fully Sparse Framework for Cooperative Object Detection
Yunshuang Yuan, Yan Xia, Daniel Cremers +1
Cooperative perception can increase the view field and decrease the occlusion of an ego vehicle, hence improving the perception performance and safety of autonomous driving. Despit…
Semantic Segmentation for Sequential Historical Maps by Learning from Only One Map
Yunshuang Yuan, Frank Thiemann, Monika Sester
Historical maps are valuable resources that capture detailed geographical information from the past. However, these maps are typically available in printed formats, which are not c…
StreamLTS: Query-based Temporal-Spatial LiDAR Fusion for Cooperative Object Detection
Yunshuang Yuan, Monika Sester
Cooperative perception via communication among intelligent traffic agents has great potential to improve the safety of autonomous driving. However, limited communication bandwidth,…
CoSense3D: an Agent-based Efficient Learning Framework for Collective Perception
Yunshuang Yuan, Monika Sester
Collective Perception has attracted significant attention in recent years due to its advantage for mitigating occlusion and expanding the field-of-view, thereby enhancing reliabili…
V2X-Real: a Large-Scale Dataset for Vehicle-to-Everything Cooperative Perception
Hao Xiang, Zhaoliang Zheng, Xin Xia +15
Recent advancements in Vehicle-to-Everything (V2X) technologies have enabled autonomous vehicles to share sensing information to see through occlusions, greatly boosting the percep…
Generating Evidential BEV Maps in Continuous Driving Space
Yunshuang Yuan, Hao Cheng, Michael Ying Yang +1
Safety is critical for autonomous driving, and one aspect of improving safety is to accurately capture the uncertainties of the perception system, especially knowing the unknown. D…
Leveraging Dynamic Objects for Relative Localization Correction in a Connected Autonomous Vehicle Network
Yunshuang Yuan, Monika Sester
High-accurate localization is crucial for the safety and reliability of autonomous driving, especially for the information fusion of collective perception that aims to further impr…
Keypoints-Based Deep Feature Fusion for Cooperative Vehicle Detection of Autonomous Driving
Yunshuang Yuan, Hao Cheng, Monika Sester
Sharing collective perception messages (CPM) between vehicles is investigated to decrease occlusions so as to improve the perception accuracy and safety of autonomous driving. Howe…