Waymo LLC

Facebook, Inc.

Meta Platforms, Inc.

Romain Thibaux

David Harrison Silver

Lars Seren Backstrom

Robert William Cathcart

David Vickrey

Yigal Dan Rubinstein

Ivan Bogun

Congrui Hetang

Alisha Saxena

Cheng Zhou

Dan Yigal Rubinstein

Ryan De Iaco

Eric Seo

Aspects of the disclosure provide for controlling an autonomous vehicle. For instance, one or more processors of one or more first systems of the autonomous vehicles may receive a signal indicating a predicted traffic stack for a lane in which the autonomous vehicle is currently traveling. In response to the received signal, costs of edges of a roadgraph between the autonomous vehicle and a location of the predicted traffic stack may be adjusted in order to encourage the autonomous vehicle to change lanes in response to the predicted traffic stack. A route may be generated to a destination based on at least one of the adjusted costs. The route may be provided to one or more second systems of the autonomous vehicle in order to control the autonomous vehicle according to the route.

Lane changes for autonomous vehicles involving traffic stacks at intersection

The technology relates to approaches for determining appropriate stopping locations at intersections for vehicles operating in a self-driving mode. While many intersections have stop lines painted on the roadway, many others have no such lines. Even if a stop line is present, the physical location may not match what is in store map data, which may be out of date due to construction or line repainting. Aspects of the technology employ a neural network that utilizes input training data and detected sensor data to perform classification, localization and uncertain estimation processes. Based on these processes, the system is able to evaluate distribution information for possible stop locations. The vehicle uses such information to determine an optimal stop point, which may or may not correspond to a stop line in the map data. This information is also used to update the existing map data, which can be shared with other vehicles.

Stop location change detection

A social networking system generates a newsfeed for a user to view when accessing the social networking system. Candidate stories associated with users of the social networking system are selected and an expected value score for each candidate story is determined. An expected value score is based on the probability of a user performing various types of interactions with a candidate story and a numerical value for each type of interaction. The numerical value for a type of interaction represents a value to the social networking system of the type of interaction. Based on the expected value scores, the candidate stories are ranked and the ranking used to select candidate stories for the newsfeed.

Arranging stories on newsfeeds based on expected value scoring on a social networking system

Aspects of the disclosure relate to detecting unmapped drivable road surfaces. In one instance, sensor data captured by a sensor of an autonomous vehicle may be projected onto a grid having a plurality of cells. The plurality of cells may be classified by generating a label for each of the plurality of cells. Each label may identifies whether or not a corresponding cell contains a drivable surface. Ones of the plurality of cells may be clustered based on the labels to form a cluster of cells. An area of the cluster of cells may be compared to a map. Whether the area of the cluster of cells is an unmapped drivable road surface may be determined based on the comparison.

Automatically detecting unmapped drivable road surfaces for autonomous vehicles

Embodiments are disclosed for detecting anomalies in time series using statistical models. In some embodiments, a linear regression model is built for the time series for predicting future values of the time series. Furthermore, the standard deviation of the difference between a prediction and an ideal value of the time series at any point is then estimated. An anomaly is detected when the difference between the prediction and the observed value is greater than a certain threshold based on the estimated standard deviation.

Detecting anomalies in a time series

Techniques to track interactions with content on a social network. In one embodiment, references are embedded within content to be presented to a user. At least one identifying reference from among the references is received in response to an interaction by the user with the content. The identifying reference is stored in a memory caching system as a record of the interaction. The references may comprise a key and a type. The key may be associated with the content and the type may be associated with components in the content. The interaction may involve selection by the user of a component within a story.

Methods and systems for tracking of user interactions with content in social networks

A social networking system generates a newsfeed for a user to view when accessing the social networking system. Candidate stories associated with users of the social networking system are selected and attributes of each story are determined. The candidate stories are ranked so that the ranking of a candidate story having one or more common attributes with another candidate story is modified. This reduces the likelihood of the newsfeed presenting candidate stories with common attributes proximate to each other.

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Fremont, CA, United States of America

Romain Thibaux