Waymo LLC

Microsoft Technology Licensing, LLC

Bradley Dodson

David A Nichols

Frank Dachille

Zhinan Xu

Jennifer Taylor

Ananya Misra

Sean Rafferty

Jiajing Wang

Chung Webster

Sandro Alighiero Young

Dillon Cower

Samuel Liu

Joel Scaria

Lukasz Kulik

Karan Mahajan

Jayant Thatte

A system includes a first autonomous vehicle (AV) including a sensing system including a set of sensors, a memory storing instructions and a processing device operatively coupled to the memory, wherein the instructions, when executed by the processing device, cause the processing device to perform operations including observing, using data obtained from the sensing system, an event reflecting a scenario within a driving environment, generating a set of event observation data characterizing the event, and causing the set of event observation data to be shared with at least a second AV that is determined to be in a vicinity of the event after the event is observed.

Real-time multi-vehicle sensing and perception

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a depth map of a scene from a camera image using a neural network. One of the methods includes obtaining an image captured by a first sensor. A neural network processes the image to generate a respective score for each of a plurality of locations in the image. Known depth data specifying respective known depth values for some of the locations in the image is obtained. A depth output is generated that assigns a depth value to some of the locations in the image, including determining whether the score for a location exceeds a threshold; and when the score exceeds the threshold and the known depth value is available for the location, assigning the known depth value for the location to the location in the depth output.

Generating depth from camera images and known depth data using neural networks

Faces may be indexed and identified using visual and social criteria. In one example, the visual features of a face are quantified, and the quantification of the features is represented in a vector. Aspects of the vector are then represented in the form of text strings. Social context surrounding the face is also represented in the form of text strings. The text strings&#x2014;both the visual-based strings and/or the social-based strings&#x2014;are associated with the face, and are stored in an index. The association of these strings with the face then may make the face text-searchable on both its visual and social features. Searches on these visual and/or social features may be used to help to assist in identifying new images of faces, or to propose tags for users to apply to photos.

Face recognition using social data

Growing community of inventors

Seattle, WA, United States of America

Bradley Dodson