arbitration graphs 1autonomous driving 1learned planning 1motion planning 1rule-based planning 1safety verification 1
From the 1 of 3 linked papers with an AI index.
3 papers
cs.RO2026
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…
cs.RO2026
Better Safe Than Sorry: Enhancing Arbitration Graphs for Safe and Robust Autonomous Decision-Making
Piotr Spieker, Nick Le Large, Martin Lauer
This paper introduces an extension to the arbitration graph framework designed to enhance the safety and robustness of autonomous systems in complex, dynamic environments. Building…
cs.RO2026
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…