Abstract: The population density for a geographic area is predicted using a Markov Random Field (MRF) model. A MRF model is defined for estimating a number of mobile devices being used within a geographic area. The MRF model includes a set of rules describing how to use current data describing mobile devices currently observed in the area, and historical data describing mobile devices historically observed in the area to produce the estimate. Values of weight parameters in the MRF model are learned using the historical data. The current and historical data are applied to the MRF model having the learned weight parameters, and cost minimization is used to estimate of the number of mobile devices currently being used within the area. This estimate is used to predict the population density for the area. The predicted population density can then be used to provide location-based services.

Abstract: A passenger in an automated vehicle may relinquish control of the vehicle to a control computer when the control computer has determined that it may maneuver the vehicle safely to a destination. The passenger may relinquish or regain control of the vehicle by applying different degrees of pressure, for example, on a steering wheel of the vehicle. The control computer may convey status information to a passenger in a variety of ways including by illuminating elements of the vehicle. The color and location of the illumination may indicate the status of the control computer, for example, whether the control computer has been armed, is ready to take control of the vehicle, or is currently controlling the vehicle.

Abstract: Autonomous vehicles use various computing systems to transport passengers from one location to another. A control computer sends messages to the various systems of the vehicle in order to maneuver the vehicle safely to the destination. The control computer may display information on an electronic display in order to allow the passenger to understand what actions the vehicle may be taking in the immediate future. Various icons and images may be used to provide this information to the passenger.

Abstract: A system and method provides maps identifying the 3D location of traffic lights. The position, location, and orientation of a traffic light may be automatically extrapolated from two or more images. The maps may then be used to assist robotic vehicles or human drivers to identify the location and status of a traffic signal.

Abstract: Autonomous vehicles use various computing systems to transport passengers from one location to another. A control computer sends messages to the various systems of the vehicle in order to maneuver the vehicle safely to the destination. The control computer may display information on an electronic display in order to allow the passenger to understand what actions the vehicle may be taking in the immediate future. Various icons and images may be used to provide this information to the passenger.

Abstract: A variety of methods, systems, devices and arrangements are implemented for use with motion capture. One such method is implemented for identifying salient points from three-dimensional image data. The method involves the execution of instructions on a computer system to generate a three-dimensional surface mesh from the three-dimensional image data. Lengths of possible paths from a plurality of points on the three-dimensional surface mesh to a common reference point are categorized. The categorized lengths of possible paths are used to identify a subset of the plurality of points as salient points.

Abstract: According to various embodiments, a user interface (UI) includes a precedence graph area and an icon list displaying multiple types of program icons. A user selection of one of the program icons is received, the user selection corresponding to moving the selected program icon to the precedence graph area, the selected program icon referencing a composer UI to generate content of a specific media type. The selected program icon is characterized as a first program-content-pairing icon that references the content created by the composer UI. Thereafter, a user interaction with a plurality of program-content-pairing icons in the precedence graph area is detected, the user interaction corresponding to specifying an ordering of the plurality of program-content-pairing icons. A program flow precedence graph referencing a program flow of an interactive program is the generated, based on the ordering of the program-content-pairing icons in the precedence graph area.

Abstract: A system and method provides maps identifying the 3D location of traffic lights. The position, location, and orientation of a traffic light may be automatically extrapolated from two or more images. The maps may then be used to assist robotic vehicles or human drivers to identify the location and status of a traffic signal.

Abstract: Autonomous vehicles use various computing systems to transport passengers from one location to another. A control computer sends messages to the various systems of the vehicle in order to maneuver the vehicle safely to the destination. The control computer may display information on an electronic display in order to allow the passenger to understand what actions the vehicle may be taking in the immediate future. Various icons and images may be used to provide this information to the passenger.

Abstract: A passenger in an automated vehicle may relinquish control of the vehicle to a control computer when the control computer has determined that it may maneuver the vehicle safely to a destination. The passenger may relinquish or regain control of the vehicle by applying different degrees of pressure, for example, on a steering wheel of the vehicle. The control computer may convey status information to a passenger in a variety of ways including by illuminating elements of the vehicle. The color and location of the illumination may indicate the status of the control computer, for example, whether the control computer has been armed, is ready to take control of the vehicle, or is currently controlling the vehicle.

Abstract: A system and method provides maps identifying the 3D location of traffic lights. The position, location, and orientation of a traffic light may be automatically extrapolated from two or more images. The maps may then be used to assist robotic vehicles or human drivers to identify the location and status of a traffic signal.

Abstract: The present invention relates generally to arranging for free or discounted transportation to an advertiser's business location. More specifically, the invention involves automatically comparing the cost of transportation and the potential profit from a completed transaction using a number of real-time calculations. For example, the calculation may consider various factors including a consumer's current location, the consumer's most likely route and form of transportation (such as train, personal car, taxi, rental car, or shared vehicle), the consumer's daily agenda, the price competing advertisers are willing to pay for the customer to be delivered to alternate locations, and other costs. In this regard, the customer's obstacles to entering a business location are reduced while routing and cost calculations are automatically handled based on the demand for the advertiser's goods and potential profit margins.

Abstract: A variety of methods, systems, devices and arrangements are implemented for use with motion capture. One such method is implemented for identifying salient points from three-dimensional image data. The method involves the execution of instructions on a computer system to generate a three-dimensional surface mesh from the three-dimensional image data. Lengths of possible paths from a plurality of points on the three-dimensional surface mesh to a common reference point are categorized. The categorized lengths of possible paths are used to identify a subset of the plurality of points as salient points.

Abstract: Aspects of the disclosure relate generally to an autonomous vehicle accessing portions of a map to localize itself within the map. More specifically, one or more convolution scores may be generated between a prior map and a current map. Convolution scores may be generated by applying a fast Fourier transform on both the prior and current maps, multiplying the results of the transforms, and taking the inverse fast Fourier transform of the product. Based on these convolution scores, an autonomous vehicle may determine the offset between the maps and localize itself relative to the prior map.

Abstract: Aspects of the present disclosure relate generally to indoor localization, for example, where GPS or other localization signals are unavailable. More specifically, aspects relate to using a particle filter in conjunction with a gyroscope and/or accelerometer to identify a current location of a client device with respect to a map. In one example, the map may be based upon a map including a series of walls representing locations where a user may not walk within a building. In another example, the map may be based upon a series of rails representing locations where a user may walk within a building.

Abstract: A system and method provides maps identifying the 3D location of traffic lights. The position, location, and orientation of a traffic light may be automatically extrapolated from two or more images. The maps may then be used to assist robotic vehicles or human drivers to identify the location and status of a traffic signal.

Abstract: Aspects of the present disclosure relate generally to indoor localization, for example, where GPS or other localization signals are unavailable. More specifically, aspects relate to using a particle filter in conjunction with one or more orientation devices to identify a location of a client device with respect to a map of an indoor space. This location may then be used to identify the path of the client device through the indoor space.

Abstract: Aspects of the present disclosure relate generally to indoor localization, for example, where GPS or other localization signals are unavailable. More particularly, the estimated location, and in some examples the estimated heading, of a client device may be displayed on a display of the client device. As the device is moved through the indoor space, its location and/or orientation may be estimated based on measurements from one or more orientation devices. Typically, as the client device moves through an indoor space, the location estimation may become less and less accurate. This may be addressed by allowing the user to correct the current location and/or heading. The correction may be logged by the client device and transmitted to a server for further processing.

Abstract: An electronic device includes a band configured to be worn on the head of a user. The band has a central portion and first and second temple portions extending therefrom. The central portion is configured to contact a portion of the face of a user, and the temple portions are configured to contact portions of the head of the user near ears thereof. An operational unit having a display element is affixed to the first temple portion and includes a housing having an arm defining a longitudinal axis. The housing also includes an elbow portion defining a display end that supports the display element such that it extends along a display axis angled with respect to the longitudinal axis and such that the display element is positionable over an eye of the user. Image generating means are disposed within the housing for generating an image presentable on the display element.

Abstract: The population density for a geographic area is predicted using a Markov Random Field (MRF) model. A MRF model is defined for estimating a number of mobile devices being used within a geographic area. The MRF model includes a set of rules describing how to use current data describing mobile devices currently observed in the area, and historical data describing mobile devices historically observed in the area to produce the estimate. Values of weight parameters in the MRF model are learned using the historical data. The current and historical data are applied to the MRF model having the learned weight parameters, and cost minimization is used to estimate of the number of mobile devices currently being used within the area. This estimate is used to predict the population density for the area. The predicted population density can then be used to provide location-based services.