7 citations · 37 across the 27 of their papers we have counts for
23 papers · 1 filter
UniMetric: Universal Metric Scale Recovery for Visual Geometry Foundation Models
Lei Yang, Yuner Zhang, Ziyang Liu +5
Recent advances in multimodal foundation models, exemplified by GPT-6 Astra, raise the prospect of general-purpose embodied reasoning and planning. Using Internet videos for embodi…
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…
InfraOcc: An Infrastructure Occupancy Benchmark with Static-to-Dynamic Reasoning
Lei Yang, Xiaokai Bai, Boqi Li +8
Fixed-viewpoint infrastructure sensors repeatedly observe the same traffic space, making roadside 3D occupancy structurally different from ego-vehicle perception: a near-persistent…
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…
Stable at Any Speed: Speed-Driven Multi-Object Tracking with Learnable Kalman Filtering
Yan Gong, Mengjun Chen, Hao Liu +5
Multi-object tracking (MOT) enables autonomous vehicles to continuously perceive dynamic objects, supplying essential temporal cues for prediction, behavior understanding, and safe…
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…
Collaborative Perception Datasets for Autonomous Driving: A Review
Naibang Wang, Deyong Shang, Yan Gong +6
Collaborative perception has attracted growing interest from academia and industry due to its potential to enhance perception accuracy, safety, and robustness in autonomous driving…
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…
FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection
Guoxin Zhang, Ziying Song, Lin Liu +1
Multimodal 3D object detection has garnered considerable interest in autonomous driving. However, multimodal detectors suffer from dimension mismatches that derive from fusing 3D p…
ComDrive: Comfort-Oriented End-to-End Autonomous Driving
Junming Wang, Xingyu Zhang, Zebin Xing +7
We propose ComDrive: the first comfort-oriented end-to-end autonomous driving system to generate temporally consistent and comfortable trajectories. Recent studies have demonstrate…
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…
M2DA: Multi-Modal Fusion Transformer Incorporating Driver Attention for Autonomous Driving
Dongyang Xu, Haokun Li, Qingfan Wang +3
End-to-end autonomous driving has witnessed remarkable progress. However, the extensive deployment of autonomous vehicles has yet to be realized, primarily due to 1) inefficient mu…
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…
Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autonomous Driving
Xinyu Zhang, Li Wang, Jian Chen +11
Radar has stronger adaptability in adverse scenarios for autonomous driving environmental perception compared to widely adopted cameras and LiDARs. Compared with commonly used 3D r…
Fuzzy-NMS: Improving 3D Object Detection with Fuzzy Classification in NMS
Li Wang, Xinyu Zhang, Fachuan Zhao +6
Non-maximum suppression (NMS) is an essential post-processing module used in many 3D object detection frameworks to remove overlapping candidate bounding boxes. However, an overrel…
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…