3 papers
cs.RO2026
Don't double it: Efficient Agent Prediction in Occlusions
Anna Rothenhäusler, Markus Mazzola, Andreas Look +2
Occluded traffic agents pose a significant challenge for autonomous vehicles, as hidden pedestrians or vehicles can appear unexpectedly, yet this problem remains understudied. Exis…
cs.RO2025
AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework
Yu Yao, Salil Bhatnagar, Markus Mazzola +5
Rare, yet critical, scenarios pose a significant challenge in testing and evaluating autonomous driving planners. Relying solely on real-world driving scenes requires collecting ma…
cs.RO2023
Scaling Planning for Automated Driving using Simplistic Synthetic Data
Martin Stoll, Markus Mazzola, Maxim Dolgov +2
We challenge the perceived consensus that the application of deep learning to solve the automated driving planning task necessarily requires huge amounts of real-world data or high…