Apple Inc.

Beijing Voyager Technology Co., Ltd.

Didi Research America, LLC

Xufeng Han

Ronald Keryuan Huang

Michael P Dal Santo

Richard B Warren

Hyo Jeong Shin

Krishna M Behara

Marcos Regis Vescovi

Patrick Thomas Dillon

Lukas Michael Marti

Tingbo Hou

Kjell Fredrik Larsson

Ross W Anderson

Weiyu Zhang

Chengan Hou

Computer-implemented methods, computer-readable storage media storing instructions and computer systems for labeling significant locations based on contextual data can be implemented to perform operations that include determining a location of a computing device, and determining a label for the determined location based on contextual data associated with the significant location. The location can be a significant location that has meaning to a user of the device.

Labeling a significant location based on contextual data

Sensor information and map information may be obtained. The sensor information may characterize positions of objects in an environment of a sensor. The map information may characterize a road configuration in an environment of a vehicle. A sensor range configuration for the vehicle may be determined based on the road configuration in the environment of the vehicle. A portion of the sensor information may be processed for vehicle navigation based on the sensor range configuration.

Dynamic sensor range detection for vehicle navigation

A method includes capturing images at a device and analyzing the images at the device using a first analysis model to obtain information regarding an observed object that corresponds to a predetermined object. The method also includes determining whether the information regarding the observed object is consistent with stored mapping information, and, in response to determining that the information regarding the observed object is not consistent with the stored mapping information, modifying the stored mapping information based on the observed object.

Distributed collection and verification of map information

Systems and methods are provided for calibrating light intensity. An exemplary method for light intensity calibration may comprise: obtaining a plurality of intensity distributions of reflected light from an area, wherein each of the intensity distributions is associated with a beam; determining a reference intensity distribution from the plurality of intensity distributions, wherein the reference intensity distribution is associated with a reference beam, the plurality of intensity distributions excluding the reference intensity distribution are non-reference intensity distributions, and the non-reference intensity distributions are each associated with a non-reference beam; and aligning each of the non-reference intensity distributions to the reference distribution to calibrate the non-reference intensity distributions against the reference distribution.

System and method for calibrating light intensity

Methods and computer products can provide personalized content based on historical interaction with a mobile device. A computing device can receive information about a user interaction with an application running on the mobile device at a first time and location. A type of the application can be identified by parsing a description of the application (e.g., using a natural language processing algorithm). An affinity model can be generated that associates the type of the application with the first time and/or location. At a second time and location, it can be determined that the second time corresponds to the first time and/or that the second location corresponds to the first location. Using the affinity model, the second time and/or location can be associated with the type of the application, and the mobile device may then display content related to the type of the application.

Providing personalized content based on historical interaction with a mobile device

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Cupertino, CA, United States of America

Xufeng Han