Google Inc.

Waymo LLC

Wei Chai

Bahman Rabii

Gaofeng Zhao

Hui Tan

Yingwei Cui

Khaled Refaat

Haoyu Chen

Kai Ding

Minfa Wang

Hrishikesh Balkrishna Aradhye

Ankit K Jain

Stephane Ross

Gregory Sean Corrado

Anna Lynn Patterson

Tushar Deepak Chandra

Tal Shaked

Jeremiah Harmsen

Huazhong Ning

Glen Anderson

Pannag Raghunath Sanketi

Benjamin Sapp

Maher Mneimneh

Mustafa Ispir

Jindong Chen

Benjamin James Caine

Alisha Saxena

Xiaobing Liu

Heng-Tze Cheng

Mehmet Levent Koc

Rohan Anil

Wesley Robert Tarle

Claire Cui

Liting Sun

Scott Morgan Ettinger

Rebecca Dawn Roelofs

Ibrahim Elbouchikhi

Sachin Kotwani

Methods, systems, and apparatus for predicting future trajectories of agents in an environment. In one aspect, a system comprises one or more computers configured to receive a data set comprising multiple training examples. The training examples include scene data comprising respective agent data for multiple agents and a ground truth trajectory for a target agent that represents ground truth motion of the target agent after a corresponding time point. The one or more computers obtain data identifying one or more of the multiple agents as non-causal agents for each training example. A non-causal agent is an agent whose states do not cause the ground truth trajectory for the target agent to change. The one or more computers generate a respective modified training example from each of the multiple training examples.

Robust behavior prediction neural networks through non-causal agent based augmentation

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing knowledge distillation for autonomous vehicles. One of the methods includes obtaining sensor data characterizing an environment, wherein the sensor data has been captured by one or more sensors on-board a vehicle in the environment; processing, for each of one or more surrounding agents in the environment, a network input generated from the sensor data using a neural network to generate an agent discomfort prediction that characterizes a level of discomfort of the agent; combining the one or more agent discomfort predictions to generate an aggregated discomfort score; and providing the aggregated discomfort score to a path planning system of the vehicle in order to generate a future path of the vehicle.

Knowledge distillation for autonomous vehicles

Provided is an application development platform and associated software development kits ('SDKs') that provide comprehensive services for generation, deployment, and management of machine-learned models used by computer applications such as, for example, mobile applications executed by a mobile computing device. In particular, the application development platform and SDKs can provide or otherwise leverage a unified, cross-platform application programming interface (“API”) that enables access to all of the different machine learning services needed for full machine learning functionality within the application. In such fashion, developers can have access to a single SDK for all machine learning services.

Application development platform and software development kits that provide comprehensive machine learning services

Systems and methods of evaluating information in a computer network environment are provided. A data processing system can obtain or receive a content placement criterion, such as a keyword, associated with a content item and can determine a quality metric of the content placement criterion. The data processing system can identify a candidate content placement criterion and expand placement criteria associated with the content item to include the content placement criterion and the candidate content placement criterion based at least in part on an evaluation of the quality metric of the content placement criterion. The data processing system can expand placement criteria based in part on a throttling parameter. The data processing system can identify a correlation between a document and the placement criteria to identify appropriate content items for the document.

Expansion of high performing placement criteria

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying high-priority agents in the vicinity of a vehicle. The high-priority agents can be identified based on a set of mutual importance scores in which each mutual importance score indicates an estimated mutual relevance between the vehicle and a different agent from a set of agents on planning decisions of the other. The mutual importance scores can be calculated based on importance scores assessed from the perspectives of both the vehicle and the agents.

Ranking agents near autonomous vehicles by mutual importance

Data representing a set of predicted trajectories and a planned trajectory for an autonomous vehicle is obtained. A predictability score for the planned trajectory can be determined based on a comparison of the planned trajectory to the set of predicted trajectories for the autonomous vehicle. The predictability score indicates a level of predictability of the planned trajectory. A determination can be made, based at least on the predictability score, whether to initiate travel with the autonomous vehicle along the planned trajectory. In response to determining to initiate travel with the autonomous vehicle along the planned trajectory, a control system can be directed to maneuver the autonomous vehicle along the planned trajectory.

Predictability-based autonomous vehicle trajectory assessments

A system includes one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the computers to implement a combined machine learning model for processing an input including multiple features to generate a predicted output for the machine learning input. The combined model includes: a deep machine learning model configured to process the features to generate a deep model output; a wide machine learning model configured to process the features to generate a wide model output; and a combining layer configured to process the deep model output generated by the deep machine learning model and the wide model output generated by the wide machine learning model to generate the predicted output, in which the deep model and the wide model have been trained jointly on training data to generate the deep model output and the wide model output.

Wide and deep machine learning models

A system and method of recommending a bundle of content items to a user, including storing a plurality of content items in a computer system, determining a respective co-selection score for each pair of content items among the plurality of content items, the co-selection score indicating a probability that a given pair of content items among the plurality of content items will both be downloaded by a user of the computer system, and outputting, to a first user, a plurality of content items comprising a sub-set of the plurality of content items.

System and method to recommend a bundle of items based on item/user tagging and co-install graph

Systems and methods are disclosed for determining media consumption preferences. A method may include accessing media consumption history associated with a user. The media consumption history may include at least one of media purchase history of the user, media viewing history of the user, and media listening history of the user. A media category preference of the user may be determined, based on the media consumption history. The media category preference may include a popularity indication for each of a plurality of media categories of media items in the media consumption history. Search results provided in response to a search query by the user and/or media recommendations prepared for the user may be scored based on the media category preference. The media may include a video, a movie, a TV show, a book, an audio recording, a music album and/or another type of digital media.

Determining media consumption preferences

Growing community of inventors

Cupertino, CA, United States of America

Wei Chai