5 papers · 1 filter
NavMapFusion: Diffusion-based Fusion of Navigation Maps for Online Vectorized HD Map Construction
Thomas Monninger, Zihan Zhang, Steffen Staab +1
Accurate environmental representations are essential for autonomous driving, providing the foundation for safe and efficient navigation. Traditionally, high-definition (HD) maps ar…
AugMapNet: Improving Spatial Latent Structure via BEV Grid Augmentation for Enhanced Vectorized Online HD Map Construction
Thomas Monninger, Md Zafar Anwar, Stanislaw Antol +2
Autonomous driving requires understanding infrastructure elements, such as lanes and crosswalks. To navigate safely, this understanding must be derived from sensor data in real-tim…
MapDiffusion: Generative Diffusion for Vectorized Online HD Map Construction and Uncertainty Estimation in Autonomous Driving
Thomas Monninger, Zihan Zhang, Zhipeng Mo +3
Autonomous driving requires an understanding of the static environment from sensor data. Learned Bird's-Eye View (BEV) encoders are commonly used to fuse multiple inputs, and a vec…
LMT-Net: Lane Model Transformer Network for Automated HD Mapping from Sparse Vehicle Observations
Michael Mink, Thomas Monninger, Steffen Staab
In autonomous driving, High Definition (HD) maps provide a complete lane model that is not limited by sensor range and occlusions. However, the generation and upkeep of HD maps inv…
TempBEV: Improving Learned BEV Encoders with Combined Image and BEV Space Temporal Aggregation
Thomas Monninger, Vandana Dokkadi, Md Zafar Anwar +1
Autonomous driving requires an accurate representation of the environment. A strategy toward high accuracy is to fuse data from several sensors. Learned Bird's-Eye View (BEV) encod…