6 citations · 8 across the 6 of their papers we have counts for
7 papers
AutoScale: Linear Scalarization Guided by Multi-Task Optimization Metrics
Yi Yang, Kei Ikemura, Qingwen Zhang +5
Recent multi-task learning studies suggest that linear scalarization, when using well-chosen fixed task weights, can achieve comparable to or even better performance than complex m…
HiMo: High-Speed Objects Motion Compensation in Point Clouds
Qingwen Zhang, Ajinkya Khoche, Yi Yang +4
LiDAR point cloud is essential for autonomous vehicles, but motion distortions from dynamic objects degrade the data quality. While previous work has considered distortions caused…
SeFlow: A Self-Supervised Scene Flow Method in Autonomous Driving
Qingwen Zhang, Yi Yang, Peizheng Li +2
Scene flow estimation predicts the 3D motion at each point in successive LiDAR scans. This detailed, point-level, information can help autonomous vehicles to accurately predict and…
Hard Cases Detection in Motion Prediction by Vision-Language Foundation Models
Yi Yang, Qingwen Zhang, Kei Ikemura +2
Addressing hard cases in autonomous driving, such as anomalous road users, extreme weather conditions, and complex traffic interactions, presents significant challenges. To ensure…
DeFlow: Decoder of Scene Flow Network in Autonomous Driving
Qingwen Zhang, Yi Yang, Heng Fang +2
Scene flow estimation determines a scene's 3D motion field, by predicting the motion of points in the scene, especially for aiding tasks in autonomous driving. Many networks with l…
Human-Centric Autonomous Systems With LLMs for User Command Reasoning
Yi Yang, Qingwen Zhang, Ci Li +3
The evolution of autonomous driving has made remarkable advancements in recent years, evolving into a tangible reality. However, a human-centric large-scale adoption hinges on meet…
RMP: A Random Mask Pretrain Framework for Motion Prediction
Yi Yang, Qingwen Zhang, Thomas Gilles +2
As the pretraining technique is growing in popularity, little work has been done on pretrained learning-based motion prediction methods in autonomous driving. In this paper, we pro…