activity
20242026
most citedDGFusion: Dual-guided Fusion for Robust Multi-Modal 3D Object Detection

3 citations · 3 across the 2 of their papers we have counts for

collaborators

8 papers

cs.CV2026

VGGT-World: Transforming VGGT into an Autoregressive Geometry World Model

Xiangyu Sun, Shijie Wang, Fengyi Zhang +5

World models that forecast scene evolution by generating future video frames devote the bulk of their capacity to photometric details, yet the resulting predictions often remain ge…

cs.CV2026

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…

cs.CV20253 cited

DGFusion: Dual-guided Fusion for Robust Multi-Modal 3D Object Detection

Feiyang Jia, Caiyan Jia, Ailin Liu +6

As a critical task in autonomous driving perception systems, 3D object detection is used to identify and track key objects, such as vehicles and pedestrians. However, detecting dis…

cs.CV2025

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…

cs.CV2025

Beyond Imitation: Constraint-Aware Trajectory Generation with Flow Matching For End-to-End Autonomous Driving

Lin Liu, Guanyi Yu, Ziying Song +5

Planning is a critical component of end-to-end autonomous driving. However, prevailing imitation learning methods often suffer from mode collapse, failing to produce diverse trajec…

cs.CV2025

Fully Unified Motion Planning for End-to-End Autonomous Driving

Lin Liu, Caiyan Jia, Ziying Song +6

Current end-to-end autonomous driving methods typically learn only from expert planning data collected from a single ego vehicle, severely limiting the diversity of learnable drivi…