10 papers
Mosaic: An Extensible Framework for Composing Rule-Based and Learned Motion Planners
Nick Le Large, Marlon Steiner, Lingguang Wang +4
Mosaic is a framework that combines rule‑based and learned motion planners using arbitration graphs, separating trajectory verification from selection to improve safety and perform…
Wavelet Phase Diffusion for Structurally and Semantically Consistent Sim-to-Real Translation
Kaiwen Wang, Frank Bieder, Yinzhe Shen +3
Simulation-to-reality translation must bridge the appearance gap between synthetic and real domains while preserving structural and semantic consistency. Conditioning-based methods…
Creating Impactful Autonomous Driving Datasets: A Strategic Guide from Research Gap to Benchmark
Richard Schwarzkopf, Jonas Merkert, Frank Bieder +22
Well-designed autonomous driving datasets have fundamentally shaped research progress, yet existing literature primarily describes what datasets contain rather than how to strategi…
The Road Ahead in Autonomous Driving: The KITScenes Multimodal Dataset
Richard Schwarzkopf, Fabian Immel, Alexander Blumberg +21
Existing autonomous driving datasets have enabled major progress, but fall short in sensor fidelity, map completeness, or geographic diversity. We present KITScenes Multimodal, a E…
MapGCLR: Geospatial Contrastive Learning of Representations for Online Vectorized HD Map Construction
Jonas Merkert, Alexander Blumberg, Jan-Hendrik Pauls +1
Autonomous vehicles rely on map information to understand the world around them. However, the creation and maintenance of offline high-definition (HD) maps remains costly. A more s…
XD-MAP: Cross-Modal Domain Adaptation via Semantic Parametric Maps for Scalable Training Data Generation
Frank Bieder, Hendrik Königshof, Haohao Hu +4
Until open-world foundation models match the performance of specialized approaches, deep learning systems remain dependent on task- and sensor-specific data availability. To bridge…