Parting with Misconceptions about Learning-based Vehicle Motion Planning. - Daniel Dauner; Marcel Hallgarten; Andreas Geiger 001; Kashyap Chitta (CoRL, 2023)

From Prediction to Planning With Goal Conditioned Lane Graph Traversals. - Marcel Hallgarten; Martin Stoll; Andreas Zell (ITSC, 2023)

Rethinking Integration of Prediction and Planning in Deep Learning-Based Automated Driving Systems: A Review. - Steffen Hagedorn; Marcel Hallgarten; Martin Stoll; Alexandru Condurache (CoRR, 2023)

Conditional Unscented Autoencoders for Trajectory Prediction. - Faris Janjos; Marcel Hallgarten; Anthony Knittel; Maxim Dolgov; Andreas Zell; J. Marius Zöllner (ECCV Workshops (7), 2024)

Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios? - Marcel Hallgarten; Julián Zapata; Martin Stoll; Katrin Renz; Andreas Zell (IROS, 2024)

Stay on Track: A Frenet Wrapper to Overcome Off-road Trajectories in Vehicle Motion Prediction. - Marcel Hallgarten; Ismail Kisa; Martin Stoll; Andreas Zell (IV, 2024)

NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking. - Daniel Dauner; Marcel Hallgarten; Tianyu Li; Xinshuo Weng; Zhiyu Huang; Zetong Yang; Hongyang Li 001; Igor Gilitschenski; Boris Ivanovic; Marco Pavone 001; Andreas Geiger 001; Kashyap Chitta (NeurIPS, 2024)

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback. - Steffen Hagedorn; Aron Distelzweig; Marcel Hallgarten; Alexandru Condurache (CoRR, 2025)

Pseudo-Simulation for Autonomous Driving. - Wei Cao; Marcel Hallgarten; Tianyu Li; Daniel Dauner; Xunjiang Gu; Caojun Wang; Yakov Miron; Marco Aiello 001; Hongyang Li 001; Igor Gilitschenski; Boris Ivanovic; Marco Pavone 001; et al. (CoRR, 2025)

AGENTS-LLM: Augmentative GENeration of Challenging Traffic Scenarios with an Agentic LLM Framework. - Yu Yao; Salil Bhatnagar; Markus Mazzola; Vasileios Belagiannis; Igor Gilitschenski; Luigi Palmieri; Simon Razniewski; Marcel Hallgarten (CoRR, 2025)