654 results for "uber estimate" across all locations
HDNET: Exploiting HD Maps for 3D Object Detection
B. Yang, M. Liang, R. Urtasun
In this paper we show that High-Definition (HD) maps provide strong priors that can boost the performance and robustness of modern 3D object detectors. Towards this goal, we design a single stage detector that extracts geometric and semantic features from the HD maps. […] [PDF]
Conference on Robot Learning (CORL), 2018
Discover Kochi
Whether you’re new to the city, still needing maps to find your way around, or an old-timer with a stock of stories about the good ol’ days in Kochi
New Year’s Eve Ride Guide
Bubbly is flowing, fireworks are sparkling, and you’re celebrating the end of the year with your friends and family. Follow these tips and the only thing you’ll have to worry about is keeping your resolutions in 2016!
2018–19 New Year’s Eve ride guide
Here are a few ways that Uber can help you start the new year with a reliable ride.
PIXOR: Real-time 3D Object Detection from Point Clouds
B. Yang, W. Luo, R. Urtasun
We address the problem of real-time 3D object detection from point clouds in the context of autonomous driving. […] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2018
You asked, we listened: announcing the new Sea-Tac Airport lot
Learn all about the exciting new features coming to the new Sea-Tac Airport holding lot exclusive for Uber driver-partners.
Voyagez à plusieurs : – 50% sur vos courses en Van !
À l’occasion de la journée internationale du co-voiturage, Uber vous offre 50% de réduction sur toutes vos courses en van payées à plusieurs pendant 10 jours !
DAGMapper: Learning to Map by Discovering Lane Topology
N. Homayounfar, W.-C. Ma*, J. Liang*, X. Wu, J. Fan, R. Urtasun
We map complex lane topologies in highways by formulating the problem as a deep directed graphical model. As an interesting result, we can train our model in I40 and generalize to unseen highways in SF. [PDF]
International Conference on Computer Vision (ICCV), 2019
Forecasting Interactive Dynamics of Pedestrians with Fictitious Play
W. Ma, D. Huang, N. Lee, K. Kitani
We develop predictive models of pedestrian dynamics by encoding the coupled nature of multi-pedestrian interaction using game theory, and deep learning-based visual analysis to estimate person-specific behavior parameters. Building predictive models for multi-pedestrian interactions however, is very challenging due to two reasons […] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2017
Matching Adversarial Networks
G. Mattyus, R. Urtasun
Generative Adversarial Nets (GANs) and Conditonal GANs (CGANs) show that using a trained network as loss function (discriminator) enables to synthesize highly structured outputs (e.g. natural images). However, applying a discriminator network as a universal loss function for common supervised tasks (e.g. semantic segmentation, line detection, depth estimation) is considerably less successful. […] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2018