Marcel Hallgarten — accepted AI-conference papers

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 +2

IROS 2025

Rare, yet critical, scenarios pose a significant challenge in testing and evaluating autonomous driving planners. Relying solely on real-world driving scenes requires collecting massive datasets to capture these scenarios. While automatic generation of traffic scenarios appears promising, data-drive

Cited by 4 Source ↗Scholar ↗

2025

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback

Steffen Hagedorn, Aron Distelzweig, Marcel Hallgarten, Alexandru Paul Condurache

IROS 2025

In automated driving, predicting trajectories of surrounding vehicles supports reasoning about scene dynamics and enables safe planning for the ego vehicle. However, existing models handle predictions as an instantaneous task of forecasting future trajectories based on observed information. As time

Cited by 4 Source ↗Scholar ↗

2025

Pseudo-Simulation for Autonomous Driving

Wei Cao, Marcel Hallgarten, Tianyu Li, Daniel Dauner, Xunjiang Gu, Caojun Wang +8

CoRL 2025poster

Existing evaluation paradigms for Autonomous Vehicles (AVs) face critical limitations. Real-world evaluation is often challenging due to safety concerns and a lack of reproducibility, whereas closed-loop simulation can face insufficient realism or high computational costs. Open-loop evaluation, whil…

Cited by 0 Source ↗code ↗Scholar ↗

2024

Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?

Marcel Hallgarten, Julian Zapata, Martin Stoll, Katrin Renz, Andreas Zell

IROS 2024poster

Real-world autonomous driving systems must make safe decisions in the face of rare and diverse traffic scenarios. Current state-of-the-art planners are mostly evaluated on real-world datasets like nuScenes (open-loop) or nuPlan (closed-loop). In particular nuPlan seems to be an expressive evaluation…

Cited by 12 Source ↗code ↗Scholar ↗

2024

NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking

Daniel Dauner, Marcel Hallgarten, Tianyu Li, Xinshuo Weng, Zhiyu Huang, Zetong Yang +6

NeurIPS 2024poster

Benchmarking vision-based driving policies is challenging. On one hand, open-loop evaluation with real data is easy, but these results do not reflect closed-loop performance. On the other, closed-loop evaluation is possible in simulation, but is hard to scale due to its significant computational dem…

Cited by 26 Source ↗PDF ↗code ↗Scholar ↗

2023

Parting with Misconceptions about Learning-based Vehicle Motion Planning

Daniel Dauner, Marcel Hallgarten, Andreas Geiger, Kashyap Chitta

CoRL 2023poster

The release of nuPlan marks a new era in vehicle motion planning research, offering the first large-scale real-world dataset and evaluation schemes requiring both precise short-term planning and long-horizon ego-forecasting. Existing systems struggle to simultaneously meet both requirements. Indeed,…

Cited by 141 Source ↗Scholar ↗