Juergen Luettin — accepted AI-conference papers

2026

DiffSemanticFusion: Semantic Raster BEV Fusion for Autonomous Driving via Online Map Diffusion

Zhigang Sun, Yiru Wang, Anqing Jiang, Shuo Wang, Yu Gao, Yuwen Heng +10

RA-L 2026

Autonomous driving requires accurate scene understanding, including road geometry, traffic agents, and their semantic relationships. In online HD map generation scenarios, raster-based representations are well-suited to vision models but lack geometric precision, while graph-based representations re

Cited by 1 Source ↗code ↗Scholar ↗

2024

SemanticFormer: Holistic and Semantic Traffic Scene Representation for Trajectory Prediction Using Knowledge Graphs

Zhigang Sun, Zixu Wang, Lavdim Halilaj, Juergen Luettin

RA-L 2024

Trajectory prediction in autonomous driving relies on accurate representation of all relevant contexts of the driving scene, including traffic participants, road topology, traffic signs, as well as their semantic relations to each other. Despite increased attention to this issue, most approaches in

Cited by 18 Source ↗code ↗Scholar ↗