Google Inc.

Nvidia Corporation

Deepmap Inc.

Wei Luo

Mark Damon Wheeler

Sylvanus Garnet Bent, Iii

Mehmet Levent Koc

Xiaolan Zhou

Xiaqing Wu

Gil Colgate

Marek Vanco

Example embodiments of the present disclosure provide for an example method. The example method includes generating an initial user interface including a content assistant component. The example method include obtaining user input data. The example method includes processing, by a machine learned model interfacing with the content assistant component, the data indicative of the input received from the user. The method includes obtaining output data, from the machine learned model interfacing with the content assistant component, indicative of one or more content item components. The method includes transmitting data which causes the content item components to be provided for display via an updated user interface. The method includes obtaining data indicative of user selection of approval of the content item components. The method includes generating, in response to obtaining the data indicative of the user selection of the approval of the content item components, content items.

Conversational interface for content creation and editing using large language models

The autonomous vehicle generates an overlapped image by overlaying HD map data over sensor data and rendering the overlaid images. The visualization process is repeated as the vehicle drives along the route. The visualization may be displayed on a screen within the vehicle or at a remote device. The system performs reverse rendering of a scene based on map data from a selected point. For each line of sight originating at the selected point, the system identifies the farthest object in the map data. Accordingly, the system eliminates objects obstructing the view of the farthest objects in the HD map as viewed from the selected point. The system further allows filtering of objects using filtering criteria based on semantic labels. The system generates a view from the selected point such that 3D objects matching the filtering criteria are eliminated from the view.

Visualization of high definition map data

An online system build a high definition map for geographical regions based on sensor data captured by a plurality of vehicles driving through the geographical regions. The high definition map comprises landmark map including representations of driving paths and landmarks and occupancy map comprising spatial 3D representation of the road and all physical objects around the road. The vehicles provide information to update the landmark map and the occupancy map to the online system. The vehicles detect map discrepancies based on differences in the surroundings observed using sensor data compared to the high definition map and send messages describing these map discrepancies to the online system. The online system ranks the vehicles based on factors including an upload rate of the vehicle and requests data describing high definition map from vehicles selected based on the ranking.

High definition map updates based on sensor data collected by autonomous vehicles

A computer-implemented method of organizes photographs for display on an on-line map by selecting from a plurality of photographs a candidate set of photographs based on scores of the photographs, determining clusters from the selected photographs for display on the on-line map.

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Saratoga, CA, United States of America

Wei Luo