61 citations · 64 across the 9 of their papers we have counts for
13 papers
EDCT-Bench: Uncovering Faithfulness Gaps in VLMs via Explanation-Driven Counterfactual Testing
Sihao Ding, Santosh Vasa, Aditi Ramadwar +1
Vision-Language Models (VLMs) can produce Natural Language Explanations (NLEs) that sound plausible yet remain inconsistent with the visual evidence they cite. We present Explanati…
BEVLM: Distilling Semantic Knowledge from LLMs into Bird's-Eye View Representations
Thomas Monninger, Shaoyuan Xie, Qi Alfred Chen +1
The integration of Large Language Models (LLMs) into autonomous driving has attracted growing interest for their strong reasoning and semantic understanding abilities, which are es…
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…
MapRF: Weakly Supervised Online HD Map Construction via NeRF-Guided Self-Training
Hongyu Lyu, Thomas Monninger, Julie Stephany Berrio Perez +3
Autonomous driving systems benefit from high-definition (HD) maps that provide critical information about road infrastructure. The online construction of HD maps offers a scalable…
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…
AutoVDC: Automated Vision Data Cleaning Using Vision-Language Models
Santosh Vasa, Aditi Ramadwar, Jnana Rama Krishna Darabattula +5
Training of autonomous driving systems requires extensive datasets with precise annotations to attain robust performance. Human annotations suffer from imperfections, and multiple…
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…
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…
Semantic Map Learning of Traffic Light to Lane Assignment based on Motion Data
Thomas Monninger, Andreas Weber, Steffen Staab
Understanding which traffic light controls which lane is crucial to navigate intersections safely. Autonomous vehicles commonly rely on High Definition (HD) maps that contain infor…
LMR: Lane Distance-Based Metric for Trajectory Prediction
Julian Schmidt, Thomas Monninger, Julian Jordan +1
The development of approaches for trajectory prediction requires metrics to validate and compare their performance. Currently established metrics are based on Euclidean distance, w…
Exploring Navigation Maps for Learning-Based Motion Prediction
Julian Schmidt, Julian Jordan, Franz Gritschneder +2
The prediction of surrounding agents' motion is a key for safe autonomous driving. In this paper, we explore navigation maps as an alternative to the predominant High Definition (H…
SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks
Thomas Monninger, Julian Schmidt, Jan Rupprecht +5
Understanding traffic scenes requires considering heterogeneous information about dynamic agents and the static infrastructure. In this work we propose SCENE, a methodology to enco…