3 papers
cs.RO2026
BridgeSim: Unveiling the OL-CL Gap in End-to-End Autonomous Driving
Seth Z. Zhao, Luobin Wang, Hongwei Ruan +13
Open-loop (OL) to closed-loop (CL) gap (OL-CL gap) exists when OL-pretrained policies scoring high in OL evaluations fail to transfer effectively in closed-loop (CL) deployment. In…
cs.RO2024
Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-tuning
Zhiyu Huang, Xinshuo Weng, Maximilian Igl +5
Autonomous driving necessitates the ability to reason about future interactions between traffic agents and to make informed evaluations for planning. This paper introduces the \tex…
cs.CV2024
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…