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pro vyhledávání: '"Agro, Ben"'
Perceiving the world and forecasting its future state is a critical task for self-driving. Supervised approaches leverage annotated object labels to learn a model of the world -- traditionally with object detections and trajectory predictions, or tem
Externí odkaz:
http://arxiv.org/abs/2406.08691
Autor:
Casas, Sergio, Agro, Ben, Mao, Jiageng, Gilles, Thomas, Cui, Alexander, Li, Thomas, Urtasun, Raquel
The tasks of object detection and trajectory forecasting play a crucial role in understanding the scene for autonomous driving. These tasks are typically executed in a cascading manner, making them prone to compounding errors. Furthermore, there is u
Externí odkaz:
http://arxiv.org/abs/2406.04426
A self-driving vehicle must understand its environment to determine the appropriate action. Traditional autonomy systems rely on object detection to find the agents in the scene. However, object detection assumes a discrete set of objects and loses i
Externí odkaz:
http://arxiv.org/abs/2404.01486
In recent years, semidefinite relaxations of common optimization problems in robotics have attracted growing attention due to their ability to provide globally optimal solutions. In many cases, it was shown that specific handcrafted redundant constra
Externí odkaz:
http://arxiv.org/abs/2308.05783
Publikováno v:
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 1379-1388
A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants. Existing works either perform object detection followed by trajectory forecasting of the detected objects, or predic
Externí odkaz:
http://arxiv.org/abs/2308.01471
Publikováno v:
IEEE Robotics and Automation Letters, vol. 8, no. 4, pp. 1983-1990
Task and motion planning problems in robotics combine symbolic planning over discrete task variables with motion optimization over continuous state and action variables. Recent works such as PDDLStream have focused on optimistic planning with an incr
Externí odkaz:
http://arxiv.org/abs/2111.13144
We present a self-supervised learning approach for the semantic segmentation of lidar frames. Our method is used to train a deep point cloud segmentation architecture without any human annotation. The annotation process is automated with the combinat
Externí odkaz:
http://arxiv.org/abs/2012.05897
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