15 papers
DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving
Ziying Song, Lin Liu, Hongyu Pan +7
Most end-to-end autonomous driving methods rely on imitation learning from single expert demonstrations, often leading to conservative and homogeneous behaviors that limit generali…
GraphBEV++: Multi-Modal Feature Alignment for Autonomous Driving
Ziying Song, Caiyan Jia, Lin Liu +3
Feature misalignment in BEV perception is a critical yet often overlooked challenge in autonomous driving, especially under calibration uncertainties between LiDAR and camera senso…
GraphWorld: Long-Horizon Planning with World Models for End-to-End Autonomous Driving
Ziying Song, Caiyan Jia, Lin Liu +8
End-to-end autonomous driving has made significant progress by unifying perception, prediction, and planning within a single learning framework, achieving strong performance in sho…
DriveFuture: Future-Aware Latent World Models for Autonomous Driving
Yufeng Hong, Xiaotian Zhou, Yingyan Li +6
Existing latent world models for autonomous driving have opened a promising path toward future-aware driving intelligence. However, they typically treat future latent states as pre…
GuideFlow: Constraint-Guided Flow Matching for Planning in End-to-End Autonomous Driving
Lin Liu, Caiyan Jia, Guanyi Yu +6
Driving planning is a critical component of end-to-end (E2E) autonomous driving. However, prevailing Imitative E2E Planners often suffer from multimodal trajectory mode collapse, f…
DriveWorld-VLA: Unified Latent-Space World Modeling with Vision-Language-Action for Autonomous Driving
Feiyang jia, Lin Liu, Ziying Song +4
End-to-end (E2E) autonomous driving has recently attracted increasing interest in unifying Vision-Language-Action (VLA) with World Models to enhance decision-making and forward-loo…