8 papers
Texture-Shape Bias Balancing for Robust Synthetic-to-Real Semantic Segmentation in Automotive NIR Imagery
Felix Stillger, Ben Hamscher, Lukas Hahn +3
Semantic segmentation is a fundamental component of visual perception in modern automotive systems, enabling pixel-level scene understanding. Near-Infrared imaging (NIR) offers sta…
Goal-Oriented Reactive Simulation for Closed-Loop Trajectory Prediction
Harsh Yadav, Tobias Meisen
Current trajectory prediction models are primarily trained in an open-loop manner, which often leads to covariate shift and compounding errors when deployed in real-world, closed-l…
InCaRPose: In-Cabin Relative Camera Pose Estimation Model and Dataset
Felix Stillger, Lukas Hahn, Frederik Hasecke +1
Camera extrinsic calibration is a fundamental task in computer vision. However, precise relative pose estimation in constrained, highly distorted environments, such as in-cabin aut…
Rectify, Don't Regret: On-Policy Closed-Loop Training for Multimodal Trajectory Prediction
Harsh Yadav, Christian Bohn, Tobias Meisen
Current trajectory prediction models are primarily trained in an open-loop manner, which often leads to covariate shift and compounding errors when deployed in real-world, closed-l…
Failure Modes for Deep Learning-Based Online Mapping: How to Measure and Address Them
Michael Hubbertz, Qi Han, Tobias Meisen
Deep learning-based online mapping has emerged as a cornerstone of autonomous driving, yet these models frequently fail to generalize beyond familiar environments. We propose a fra…
Faster Training, Fewer Labels: Self-Supervised Pretraining for Fine-Grained BEV Segmentation
Daniel Busch, Christian Bohn, Thomas Kurbiel +3
Dense Bird's Eye View (BEV) semantic maps are central to autonomous driving, yet current multi-camera methods depend on costly, inconsistently annotated BEV ground truth. We addres…