7 papers
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…
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…
LongTail Driving Scenarios with Reasoning Traces: The KITScenes LongTail Dataset
Royden Wagner, Omer Sahin Tas, Jaime Villa +18
In real-world domains such as self-driving, generalization to rare scenarios remains a fundamental challenge. To address this, we introduce a new dataset designed for end-to-end dr…
Impact of Localization Errors on Label Quality for Online HD Map Construction
Alexander Blumberg, Jonas Merkert, Richard Fehler +4
High-definition (HD) maps are crucial for autonomous vehicles, but their creation and maintenance is very costly. This motivates the idea of online HD map construction. To provide…
SDTagNet: Leveraging Text-Annotated Navigation Maps for Online HD Map Construction
Fabian Immel, Jan-Hendrik Pauls, Richard Fehler +3
Autonomous vehicles rely on detailed and accurate environmental information to operate safely. High definition (HD) maps offer a promising solution, but their high maintenance cost…