Zobrazeno 1 - 10
of 1 426
pro vyhledávání: '"Zhang, Qingwen"'
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 understand dynamic changes in their surroundings. Current state-of-th
Externí odkaz:
http://arxiv.org/abs/2407.01702
Addressing hard cases in autonomous driving, such as anomalous road users, extreme weather conditions, and complex traffic interactions, presents significant challenges. To ensure safety, it is crucial to detect and manage these scenarios effectively
Externí odkaz:
http://arxiv.org/abs/2405.20991
Global point clouds that correctly represent the static environment features can facilitate accurate localization and robust path planning. However, dynamic objects introduce undesired ghost tracks that are mixed up with the static environment. Exist
Externí odkaz:
http://arxiv.org/abs/2405.07283
Autor:
Zhang, Qingwen, Wang, Wenjia
Calibration refers to the statistical estimation of unknown model parameters in computer experiments, such that computer experiments can match underlying physical systems. This work develops a new calibration method for imperfect computer models, Sob
Externí odkaz:
http://arxiv.org/abs/2404.00630
The dynamic nature of the real world is one of the main challenges in robotics. The first step in dealing with it is to detect which parts of the world are dynamic. A typical benchmark task is to create a map that contains only the static part of the
Externí odkaz:
http://arxiv.org/abs/2403.01449
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 large-scale point clouds as input use voxelization to create a pseudo-i
Externí odkaz:
http://arxiv.org/abs/2401.16122
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 meeting a variety of multifaceted requirements. To ensure that the autonom
Externí odkaz:
http://arxiv.org/abs/2311.08206
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 propose a framework to formalize the pretraining task for trajectory pred
Externí odkaz:
http://arxiv.org/abs/2309.08989
In the field of robotics, the point cloud has become an essential map representation. From the perspective of downstream tasks like localization and global path planning, points corresponding to dynamic objects will adversely affect their performance
Externí odkaz:
http://arxiv.org/abs/2307.07260
Safely interacting with other traffic participants is one of the core requirements for autonomous driving, especially in intersections and occlusions. Most existing approaches are designed for particular scenarios and require significant human labor
Externí odkaz:
http://arxiv.org/abs/2209.09013