9 papers
ObjectGuidedMap: An Object-Aware Model for Robust Bird's Eye View Map Prediction in High-Occlusion Scenarios
Christian Bohn, Harsh Yadav, Klaus Friedrichs +2
Accurate BEV map prediction is a critical component of autonomous driving systems, enabling downstream planning and control. Recent approaches such as Mask2Map achieve strong perfo…
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…
Inferring Driving Maps by Deep Learning-based Trail Map Extraction
Michael Hubbertz, Pascal Colling, Qi Han +1
High-definition (HD) maps offer extensive and accurate environmental information about the driving scene, making them a crucial and essential element for planning within autonomous…
Principal Component Clustering for Semantic Segmentation in Synthetic Data Generation
Felix Stillger, Frederik Hasecke, Tobias Meisen
This technical report outlines our method for generating a synthetic dataset for semantic segmentation using a latent diffusion model. Our approach eliminates the need for addition…