1 citations · 1 across the 13 of their papers we have counts for
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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…
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…
Mosaic: An Extensible Framework for Composing Rule-Based and Learned Motion Planners
Nick Le Large, Marlon Steiner, Lingguang Wang +4
Safe and explainable motion planning remains a central challenge in autonomous driving. While rule-based planners offer predictable and explainable behavior, they often fail to gra…
Radar-based Pose Optimization for HD Map Generation from Noisy Multi-Drive Vehicle Fleet Data
Alexander Blumberg, Jonas Merkert, Christoph Stiller
High-definition (HD) maps are important for autonomous driving, but their manual generation and maintenance is very expensive. This motivates the usage of an automated map generati…
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…
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…