11 papers
A2RL V\textsubscript{max}: The A2RL autonomous racing dataset for long-range, high-speed perception and multi-vehicle interaction
Marvin Klemp, Dominic Ebner, Cornelius Schröder +15
In autonomous driving development, a perception dataset is crucial, as it provides fundamental data for training, testing, and validating algorithms for an autonomous vehicle's mul…
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…
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…
Learning High-Level Decision Making with an Interaction-Aware Attention-Based Network in Autonomous Driving
Marcelo Contreras, Willi Poh, Christoph Stiller +1
Reliable learning-based high-level decision making for lane changes and speed control in automated driving must accommodate dynamically sized inputs due to varying scene traffic fl…
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…