7 papers
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…
Streaming Multi-Agent Autoregressive Diffusion Model with World State Registers
Sicheng Mo, Yuheng Li, Ziyang Leng +2
Multi-agent interactive world models should not only generate consistent observations, but also maintain world states that persist across agents and evolve across views. Existing a…
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…
Occupancy Learning with Spatiotemporal Memory
Ziyang Leng, Jiawei Yang, Wenlong Yi +1
3D occupancy becomes a promising perception representation for autonomous driving to model the surrounding environment at a fine-grained scale. However, it remains challenging to e…
BEVCon: Advancing Bird's Eye View Perception with Contrastive Learning
Ziyang Leng, Jiawei Yang, Zhicheng Ren +1
We present BEVCon, a simple yet effective contrastive learning framework designed to improve Bird's Eye View (BEV) perception in autonomous driving. BEV perception offers a top-dow…
Dreamland: Controllable World Creation with Simulator and Generative Models
Sicheng Mo, Ziyang Leng, Leon Liu +3
Large-scale video generative models can synthesize diverse and realistic visual content for dynamic world creation, but they often lack element-wise controllability, hindering thei…