5 papers
Pseudo-Simulation for Autonomous Driving
Wei Cao, Marcel Hallgarten, Tianyu Li +11
Existing evaluation paradigms for Autonomous Vehicles (AVs) face critical limitations. Real-world evaluation is often challenging due to safety concerns and a lack of reproducibili…
Driving is a Game: Combining Planning and Prediction with Bayesian Iterative Best Response
Aron Distelzweig, Yiwei Wang, Faris Janjoš +5
Autonomous driving planning systems perform nearly perfectly in routine scenarios using lightweight, rule-based methods but still struggle in dense urban traffic, where lane change…
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…
Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback
Steffen Hagedorn, Aron Distelzweig, Marcel Hallgarten +1
In automated driving, predicting trajectories of surrounding vehicles supports reasoning about scene dynamics and enables safe planning for the ego vehicle. However, existing model…
NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking
Daniel Dauner, Marcel Hallgarten, Tianyu Li +9
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 th…