3 citations · 4 across the 8 of their papers we have counts for
5 papers · 1 filter
AutoAgent0: An Agentic Runtime for Safe Closed-Loop Driving
Jiageng Liu, Seth Z. Zhao, Zewei Zhou +9
Safe autonomous driving requires more than accurate planning at a single frame: it demands reliable closed-loop execution in dynamic environments. Although end-to-end driving polic…
DreamStream: Towards Policy-Oriented Generative Simulation for End-to-End Driving
Ziyang Leng, Sicheng Mo, Seth Z. Zhao +4
Faithfully evaluating end-to-end driving policies in simulation requires observations that are not merely photo-realistic, but preserve the scene features a policy relies on to mak…
NavSafe-: Benchmarking Closed-Loop Driving Safety in Photorealistic Environments
Yuxin Bao, Hongwei Ruan, Luobin Wang +7
End-to-end (E2E) driving policies have advanced rapidly on open-loop (OL) benchmarks, yet OL evaluation cannot reveal whether a policy can withstand compounding errors, recover fro…
MDrive: Benchmarking Closed-Loop Cooperative Driving for End-to-End Multi-agent Systems
Marco Coscoy, Zewei Zhou, Seth Z. Zhao +9
Vehicle-to-Everything (V2X) communication has emerged as a promising paradigm for autonomous driving, enabling connected agents to share complementary perception information and ne…
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…