Woven Planet North America, Inc.

Lyft, Inc.

Dhruv Lamba

Forrest Samuel Briggs

James Allen-White Hoffacker

Phillip Sawbridge

Yi Lu

George James Hansel

Corey Frederick Bangs

Chi Hoon Lee

Brian Thomas McGinn

In one embodiment, a computing system may receive an uncompressed image from a camera. The computing system may generate a compressed image by performing a compression process on the uncompressed image, wherein a decompressed image may be generated as a byproduct during the compression process. The computing system may send the decompressed image to a machine-learning model that was trained using decompressed images. The computing system may generate, by the machine-learning model, an output based on the decompressed image. The computing system may provide operational instructions to a vehicle based on the output.

Low latency image processing using byproduct decompressed images

In one embodiment, a method includes, by a computing device of a first sensor receiving synchronization information from a controller. The synchronization information being generated based on a clock of the controller. The method also includes determining, based on the synchronization information, a first offset between a first clock of the first sensor and the clock of the controller; storing the first offset; and synchronizing, based on the stored first offset and the first clock of the first sensor, a first data capture by the first sensor with a second data capture by a second sensor. The first data capture and the second data capture being requested by the controller.

Systems and methods for synchronizing sensor capture

In particular embodiments, a computing system may detect a number of objects captured within an overlapping region between a first field of view associated with a first camera and a second field of view associated with a second camera. The system may determine a respective priority ranking for each of the objects. The system may select an object from the objects based on the respective priority ranking for the object. The system may determine, for the first camera, a first lighting condition associated with the first field of view. The system may determine, for the second camera, a second lighting condition associated with the second field of view. The system may determine a shared exposure time for the selected object based on the first lighting condition and the second lighting condition. The system may cause at least one image of the selected object to be captured using the shared exposure time.

Exposure coordination for multiple cameras

In one embodiment, a computing system may determine a first target region within a first field of view of a first camera and a second target region within a second field of view of a second camera. The first field of view and the second field of view may be partially overlapping. The system may determine first lighting conditions of the first target region. The system may determine a first exposure time for at least the first camera and the second camera based at least in part on the determined first lighting conditions. The system may instruct the first camera and the second camera to take pictures using the determined first exposure time.

Growing community of inventors

San Mateo, CA, United States of America

Dhruv Lamba