40 citations · 63 across the 20 of their papers we have counts for
18 papers · 1 filter
Towards robust multimodal 3D object detection via visual foundation models
Ziying Song, Lin Liu, Hongyu Pan +4
Multimodal 3D object detection is fundamental to robust perception in autonomous driving because it integrates complementary information from LiDAR and camera sensors. However, exi…
MomADv2: Reliable Temporal Memory for End-to-End Autonomous Driving
Ziying Song, Shengkai Zhang, Lin Liu +8
Long-horizon planning is critical for safe autonomous driving in complex scenarios. Existing methods improve planning continuity with temporal memory, but such memory may become in…
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…
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…
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…
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…
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…
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…
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…
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…
SparseDet: A Simple and Effective Framework for Fully Sparse LiDAR-based 3D Object Detection
Lin Liu, Ziying Song, Qiming Xia +4
LiDAR-based sparse 3D object detection plays a crucial role in autonomous driving applications due to its computational efficiency advantages. Existing methods either use the featu…
ContrastAlign: Toward Robust BEV Feature Alignment via Contrastive Learning for Multi-Modal 3D Object Detection
Ziying Song, Hongyu Pan, Feiyang Jia +8
In the field of 3D object detection tasks, fusing heterogeneous features from LiDAR and camera sensors into a unified Bird's Eye View (BEV) representation is a widely adopted parad…
GraphBEV: Towards Robust BEV Feature Alignment for Multi-Modal 3D Object Detection
Ziying Song, Lei Yang, Shaoqing Xu +5
Integrating LiDAR and camera information into Bird's-Eye-View (BEV) representation has emerged as a crucial aspect of 3D object detection in autonomous driving. However, existing m…
Robustness-Aware 3D Object Detection in Autonomous Driving: A Review and Outlook
Ziying Song, Lin Liu, Feiyang Jia +5
In the realm of modern autonomous driving, the perception system is indispensable for accurately assessing the state of the surrounding environment, thereby enabling informed predi…
RoboFusion: Towards Robust Multi-Modal 3D Object Detection via SAM
Ziying Song, Guoxing Zhang, Lin Liu +5
Multi-modal 3D object detectors are dedicated to exploring secure and reliable perception systems for autonomous driving (AD).Although achieving state-of-the-art (SOTA) performance…
VoxelNextFusion: A Simple, Unified and Effective Voxel Fusion Framework for Multi-Modal 3D Object Detection
Ziying Song, Guoxin Zhang, Jun Xie +4
LiDAR-camera fusion can enhance the performance of 3D object detection by utilizing complementary information between depth-aware LiDAR points and semantically rich images. Existin…
GraphAlign: Enhancing Accurate Feature Alignment by Graph matching for Multi-Modal 3D Object Detection
Ziying Song, Haiyue Wei, Lin Bai +2
LiDAR and cameras are complementary sensors for 3D object detection in autonomous driving. However, it is challenging to explore the unnatural interaction between point clouds and…