Google Inc.

Waymo LLC

Yu Ouyang

Shumin Zhai

Xiaojun Bi

Francoise Beaufays

Kurt Edward Partridge

Satoshi Kataoka

Ken Wakasa

Johan Schalkwyk

Ouais Alsharif

Thomas Breuel

Ciprian Ioan Chelba

Diego Melendo Casado

Dragan Zivkovic

Meltem Oktem

Mohammadinamul Hasan Sheik

Dragomir Dimitrov Anguelov

Yang Li

Yu Zhang

Vijay Vasudevan

Edward Hsiao

Jiquan Ngiam

Yin Zhou

Jiyang Gao

Pei Sun

Keisuke Kuroyanagi

Zijian Guo

Maoqing Yao

Ruichi Yu

Hang Zhao

Maya Kabkab

Yu-Han Chen

Shiri Azenkot

Yingwei Li

Ying Yin

Kurt Edward Patridge

Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for generating a distance estimate for a target object that is depicted in an image of a scene in an environment. The system obtains data specifying (i) a target portion of the image that depicts the target object detected in the image, and (ii) one or more reference portions of the image that each depict a respective reference object detected in the image. The system further obtains, for each of the one or more reference objects, a respective distance measurement for the reference object that is a measurement of a distance from the reference object to a specified location in the environment. The system processes the obtained data to generate a distance estimate for the target object that is an estimate of a distance from the target object to the specified location in the environment.

Long range distance estimation using reference objects

A method may include determining, by a computing device and based on at least one user coherency factor, a user coherency level. The coherency level may include a predicted ability of a user to comprehend information. The method may also include determining, by the computing device and based on the user coherency level, information having a complexity that satisfies the predicted ability of the user to comprehend information. The method may further include outputting, by the computing device, at least a portion of the information.

Coherency detection and information management system

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for cross input modality learning in a mobile device are disclosed. In one aspect, a method includes activating a first modality user input mode in which user inputs by way of a first modality are recognized using a first modality recognizer; and receiving a user input by way of the first modality. The method includes, obtaining, as a result of the first modality recognizer recognizing the user input, a transcription that includes a particular term; and generating an input context data structure that references at least the particular term. The method further includes, transmitting, by the first modality recognizer, the input context data structure to a second modality recognizer for use in updating a second modality recognition model associated with the second modality recognizer.

Modality learning on mobile devices

Machine-learning models are described detecting the signaling state of a traffic signaling unit. A system can obtain an image of the traffic signaling unit, and select a model of the traffic signaling unit that identifies a position of each traffic lighting element on the unit. First and second neural network inputs are processed with a neural network to generate an estimated signaling state of the traffic signaling unit. The first neural network input can represent the image of the traffic signaling unit, and the second neural network input can represent the model of the traffic signaling unit. Using the estimated signaling state of the traffic signaling unit, the system can inform a driving decision of a vehicle.

Detecting traffic signaling states with neural networks

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing point cloud data using dynamic voxelization. When deployed within an on-board system of a vehicle, processing the point cloud data using dynamic voxelization can be used to make autonomous driving decisions for the vehicle with enhanced accuracy, for example by combining representations of point cloud data characterizing a scene from multiple views of the scene.

Processing point clouds using dynamic voxelization

In some examples, a computing device includes at least one processor; and at least one module, operable by the at least one processor to: output, for display at an output device, a graphical keyboard; receive an indication of a gesture detected at a location of a presence-sensitive input device, wherein the location of the presence-sensitive input device corresponds to a location of the output device that outputs the graphical keyboard; determine, based on at least one spatial feature of the gesture that is processed by the computing device using a neural network, at least one character string, wherein the at least one spatial feature indicates at least one physical property of the gesture; and output, for display at the output device, based at least in part on the processing of the at least one spatial feature of the gesture using the neural network, the at least one character string.

Neural network for keyboard input decoding

A computing device outputs a keyboard for display, receives an indication of a first gesture to select a first sequence of one or more keys, determines a set of candidate strings based in part on the first sequence of keys, and outputs for display at least one of the set of candidate strings. The computing device receives an indication of a second gesture to select a second sequence of one or more keys, and determines that characters associated with the second sequence of keys are included in a first candidate word based at least in part on the set of candidate strings, or are included in a second candidate word not based on the first sequence of keys. The computing device modifies the set of candidate strings based at least in part on the determination and outputs for display at least one of the modified candidate strings.

Multi-gesture text input prediction

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San Jose, CA, United States of America

Yu Ouyang