Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Beijbom, Oscar"'
Autor:
Fong, Whye Kit, Mohan, Rohit, Hurtado, Juana Valeria, Zhou, Lubing, Caesar, Holger, Beijbom, Oscar, Valada, Abhinav
Panoptic scene understanding and tracking of dynamic agents are essential for robots and automated vehicles to navigate in urban environments. As LiDARs provide accurate illumination-independent geometric depictions of the scene, performing these tas
Externí odkaz:
http://arxiv.org/abs/2109.03805
Autor:
Helou, Bassam, Dusi, Aditya, Collin, Anne, Mehdipour, Noushin, Chen, Zhiliang, Lizarazo, Cristhian, Belta, Calin, Wongpiromsarn, Tichakorn, Tebbens, Radboud Duintjer, Beijbom, Oscar
Autonomous vehicles must balance a complex set of objectives. There is no consensus on how they should do so, nor on a model for specifying a desired driving behavior. We created a dataset to help address some of these questions in a limited operatin
Externí odkaz:
http://arxiv.org/abs/2107.13507
Accurately predicting the future motion of surrounding vehicles requires reasoning about the inherent uncertainty in driving behavior. This uncertainty can be loosely decoupled into lateral (e.g., keeping lane, turning) and longitudinal (e.g., accele
Externí odkaz:
http://arxiv.org/abs/2106.15004
Autor:
Caesar, Holger, Kabzan, Juraj, Tan, Kok Seang, Fong, Whye Kit, Wolff, Eric, Lang, Alex, Fletcher, Luke, Beijbom, Oscar, Omari, Sammy
In this work, we propose the world's first closed-loop ML-based planning benchmark for autonomous driving. While there is a growing body of ML-based motion planners, the lack of established datasets and metrics has limited the progress in this area.
Externí odkaz:
http://arxiv.org/abs/2106.11810
Recent works recognized lidars as an inherently streaming data source and showed that the end-to-end latency of lidar perception models can be reduced significantly by operating on wedge-shaped point cloud sectors rather then the full point cloud. Ho
Externí odkaz:
http://arxiv.org/abs/2106.07545
A high-performing object detection system plays a crucial role in autonomous driving (AD). The performance, typically evaluated in terms of mean Average Precision, does not take into account orientation and distance of the actors in the scene, which
Externí odkaz:
http://arxiv.org/abs/2010.09350
We present CoverNet, a new method for multimodal, probabilistic trajectory prediction for urban driving. Previous work has employed a variety of methods, including multimodal regression, occupancy maps, and 1-step stochastic policies. We instead fram
Externí odkaz:
http://arxiv.org/abs/1911.10298
Camera and lidar are important sensor modalities for robotics in general and self-driving cars in particular. The sensors provide complementary information offering an opportunity for tight sensor-fusion. Surprisingly, lidar-only methods outperform f
Externí odkaz:
http://arxiv.org/abs/1911.10150
Autor:
Caesar, Holger, Bankiti, Varun, Lang, Alex H., Vora, Sourabh, Liong, Venice Erin, Xu, Qiang, Krishnan, Anush, Pan, Yu, Baldan, Giancarlo, Beijbom, Oscar
Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in
Externí odkaz:
http://arxiv.org/abs/1903.11027
Object detection in point clouds is an important aspect of many robotics applications such as autonomous driving. In this paper we consider the problem of encoding a point cloud into a format appropriate for a downstream detection pipeline. Recent li
Externí odkaz:
http://arxiv.org/abs/1812.05784