From the 1 of 5 linked papers with an AI index.
5 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…
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…
Human-Aided Trajectory Planning for Automated Vehicles through Teleoperation and Arbitration Graphs
Nick Le Large, David Brecht, Willi Poh +3
Teleoperation enables remote human support of automated vehicles in scenarios where the automation is not able to find an appropriate solution. Remote assistance concepts, where op…