7 citations · 13 across the 13 of their papers we have counts for
6 papers · 1 filter
V2X-Radar: A Multi-modal Dataset with 4D Radar for Cooperative Perception
Lei Yang, Xinyu Zhang, Jun Li +10
Modern autonomous vehicle perception systems often struggle with occlusions and limited perception range. Previous studies have demonstrated the effectiveness of cooperative percep…
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…
SGV3D:Towards Scenario Generalization for Vision-based Roadside 3D Object Detection
Lei Yang, Xinyu Zhang, Jun Li +5
Roadside perception can greatly increase the safety of autonomous vehicles by extending their perception ability beyond the visual range and addressing blind spots. However, curren…
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…