Google Inc.

Deepmap Inc.

Lenord Melvix Joseph Stephen Max

Mark Damon Wheeler

Yu Zhang

Lin Yang

Derek Thomas Miller

One or more visual descriptors for a particular Point of Interest (POI) are extracted from a corpus of User-Generated Content (UGC), wherein the corpus of UGC comprises a plurality of textual content items descriptive of the particular POI. Based on the one or more visual descriptors, a machine-learned generative vision model is used to generate a query image that depicts the particular POI. A visual similarity is determined between the query image and each of a plurality of candidate images of a corpus of visual UGC that depicts the particular POI. A representation image is selected from the plurality of candidate images to represent the particular POI based on the visual similarity between the representation image and the query image.

Generative machine-learned models for identifying optimal user-generated representation images

An HD map system represents landmarks on a high definition map for autonomous vehicle navigation, including describing spatial location of lanes of a road and semantic information about each lane, and along with traffic signs and landmarks. The system generates lane lines designating lanes of roads based on, for example, mapping of camera image pixels with high probability of being on lane lines into a three-dimensional space, and locating/connecting center lines of the lane lines. The system builds a large connected network of lane elements and their connections as a lane element graph. The system also represents traffic signs based on camera images and detection and ranging sensor depth maps. These landmarks are used in building a high definition map that allows autonomous vehicles to safely navigate through their environments.

Growing community of inventors

Mountain View, CA, United States of America

Lenord Melvix Joseph Stephen Max