Drago Anguelov – Machine Learning for Autonomous Driving at Scale скачать в хорошем качестве

Drago Anguelov – Machine Learning for Autonomous Driving at Scale

Invited Talk at the Workshop on Autonomous Driving at CVPR 2020, see http://cvpr2020.wad.vision Abstract: Machine learning is key to developing a self-driving stack that can scale to a diverse set of environments without requiring exhaustive manual labeling or expert tuning. In this talk, I will describe some of the recent modeling work at Waymo that aims to capture better the inherent structure in the autonomous driving domain. I will give an update on the Waymo Open Dataset and the recently completed Challenges and highlight some of our recent work on self-supervision of perception models, as well as data-driven approaches for sensor simulation. Bio: Drago joined Waymo in 2018 to lead the Research team, which focuses on developing the state of the art in autonomous driving using machine learning. Drago spent eight years at Google; first working on 3D vision and pose estimation for StreetView, and later leading a research team which developed computer vision systems for annotating Google Photos. The team also invented popular methods such as the Inception neural network architecture, and the SSD detector, which helped win the Imagenet 2014 Classification and Detection challenges. Before Waymo, Drago led the 3D Perception team at Zoox.