Abstract: Various methods and systems are provided for authoring and presenting 3D presentations. Generally, an augmented or virtual reality device for each author, presenter and audience member includes 3D presentation software. During authoring mode, one or more authors can use 3D and/or 2D interfaces to generate a 3D presentation that choreographs behaviors of 3D assets into scenes and beats. During presentation mode, the 3D presentation is loaded in each user device, and 3D images of the 3D assets and corresponding asset behaviors are rendered among the user devices in a coordinated manner. As such, one or more presenters can navigate the scenes and beats of the 3D presentation to deliver the 3D presentation to one or more audience members wearing augmented reality headsets.

Abstract: Some embodiments include a memory array having a vertical stack of alternating insulative levels and control gate levels. Channel material extends vertically along the stack. The control gate levels comprising conductive regions. The conductive regions include at least three different materials. Charge-storage regions are adjacent the control gate levels. Charge-blocking regions are between the charge-storage regions and the conductive regions.

Abstract: Techniques are described herein for fleet electrification management. A method includes determining a composition of electric vehicles (EVs) to replace at least a portion of non-electric vehicles in a vehicle fleet while satisfying travel requirements of the vehicle fleet. The method includes estimating an energy demand of the composition of EVs. The method includes determining an electric vehicle supply equipment (EVSE) charging infrastructure to meet the estimated energy demand. The method includes providing one or more recommendations including at least one of: a fleet electrification recommendation for transitioning the vehicle fleet into the composition of EVs, or a charging infrastructure recommendation for implementing the EVSE charging infrastructure.

Abstract: Techniques are described herein for predicting popularity metrics and/or visitation metrics that are used in the selection of a point of interest (POI) for placement of an electric vehicle charging station (EVCS). The techniques involve training a machine learning model based on information obtained about POIs at which EVCSs are already installed. The information used to train the machine learning model includes, for each existing installation location: (a) visitation data that describes visitation features, and (b) popularity metrics and/or visitation metrics that have been generated for the location. When the machine learning model has been trained, the trained machine learning model predicts popularity metrics and/or visitation metrics for a POI location at which no EVCS has been installed based on the visitation data of that POI.

Abstract: Techniques are described herein for predicting popularity metrics and/or visitation metrics that are used in the selection of a point of interest (POI) for placement of an electric vehicle charging station (EVCS). The techniques involve training a machine learning model based on information obtained about POIs at which EVCSs are already installed. The information used to train the machine learning model includes, for each existing installation location: (a) visitation data that describes visitation features, and (b) popularity metrics and/or visitation metrics that have been generated for the location. When the machine learning model has been trained, the trained machine learning model predicts popularity metrics and/or visitation metrics for a POI location at which no EVCS has been installed based on the visitation data of that POI.

Abstract: Techniques are provided for dividing control of energy flow at multiple Electric Vehicle (EV) stations using the combination of a centralized controller and a plurality of decentralized controllers. The centralized controller is configured to perform: executing algorithms to generate centralized predictions for a first period of time, wherein the centralized predictions relate to energy usage at a plurality of stations, and generating one or more centralized baseline signals based on the centralized predictions. Each decentralized controller is configured to perform: receiving the one or more centralized baseline signals, monitoring interactions at a subset of the plurality of stations during the first period of time, and updating the one or more centralized baseline signals in real-time based on the interactions to produce one or more locally-updated baseline signal.

Abstract: An approach is provided for generating an electric vehicle score (EVScore), a rating scale representing, for a user, a future estimated ease of ownership and operation of an EV within a defined geographic region. The method includes receiving a plurality of regional engine inputs pertaining to a defined geographic region, and one or more user inputs pertaining to the user. The method also includes processing, via a predictive model, the plurality of regional engine inputs and the one or more user inputs to generate one or more intermediate scores for the defined geographic region. The method also includes receiving a plurality of projected inputs pertaining to projected ownership and operation costs and benefits of electric vehicles (EVs) for the defined geographic region. The method also includes processing, via a trained machine learning model, the plurality of projected inputs and the one or more intermediate scores to generate the EVS core.

Abstract: An approach is provided for generating an electric vehicle score (EVScore), a rating scale representing, for a user, a future estimated ease of ownership and operation of an EV within a defined geographic region. The method includes receiving a plurality of regional engine inputs pertaining to a defined geographic region, and one or more user inputs pertaining to the user. The method also includes processing, via a predictive model, the plurality of regional engine inputs and the one or more user inputs to generate one or more intermediate scores for the defined geographic region. The method also includes receiving a plurality of projected inputs pertaining to projected ownership and operation costs and benefits of electric vehicles (EVs) for the defined geographic region. The method also includes processing, via a trained machine learning model, the plurality of projected inputs and the one or more intermediate scores to generate the EVS core.

Abstract: Techniques are described herein for fleet electrification management. A method includes determining a composition of electric vehicles (EVs) to replace at least a portion of non-electric vehicles in a vehicle fleet while satisfying travel requirements of the vehicle fleet. The method includes estimating an energy demand of the composition of EVs. The method includes determining an electric vehicle supply equipment (EVSE) charging infrastructure to meet the estimated energy demand. The method includes providing one or more recommendations including at least one of: a fleet electrification recommendation for transitioning the vehicle fleet into the composition of EVs, or a charging infrastructure recommendation for implementing the EVSE charging infrastructure.

Abstract: Techniques are described herein for predicting popularity metrics and/or visitation metrics that are used in the selection of a point of interest (POI) for placement of an electric vehicle charging station (EVCS). The techniques involve training a machine learning model based on information obtained about POIs at which EVCSs are already installed. The information used to train the machine learning model includes, for each existing installation location: (a) visitation data that describes visitation features, and (b) popularity metrics and/or visitation metrics that have been generated for the location. When the machine learning model has been trained, the trained machine learning model predicts popularity metrics and/or visitation metrics for a POI location at which no EVCS has been installed based on the visitation data of that POI.

Abstract: Techniques are described herein for predicting popularity metrics and/or visitation metrics that are used in the selection of a point of interest (POI) for placement of an electric vehicle charging station (EVCS). The techniques involve training a machine learning model based on information obtained about POIs at which EVCSs are already installed. The information used to train the machine learning model includes, for each existing installation location: (a) visitation data that describes visitation features, and (b) popularity metrics and/or visitation metrics that have been generated for the location. When the machine learning model has been trained, the trained machine learning model predicts popularity metrics and/or visitation metrics for a POI location at which no EVCS has been installed based on the visitation data of that POI.

Abstract: Techniques are described herein for fleet electrification management. A method includes determining a composition of electric vehicles (EVs) to replace at least a portion of non-electric vehicles in a vehicle fleet while satisfying travel requirements of the vehicle fleet. The method includes estimating an energy demand of the composition of EVs. The method includes determining an electric vehicle supply equipment (EVSE) charging infrastructure to meet the estimated energy demand. The method includes providing one or more recommendations including at least one of: a fleet electrification recommendation for transitioning the vehicle fleet into the composition of EVs, or a charging infrastructure recommendation for implementing the EVSE charging infrastructure.

Abstract: Techniques are described herein for predicting popularity metrics and/or visitation metrics that are used in the selection of a point of interest (POI) for placement of an electric vehicle charging station (EVCS). The techniques involve training a machine learning model based on information obtained about POIs at which EVCSs are already installed. The information used to train the machine learning model includes, for each existing installation location: (a) visitation data that describes visitation features, and (b) popularity metrics and/or visitation metrics that have been generated for the location. When the machine learning model has been trained, the trained machine learning model predicts popularity metrics and/or visitation metrics for a POI location at which no EVCS has been installed based on the visitation data of that POI.

Abstract: An approach is provided for dynamically controlling power distribution amongst a plurality of charging station ports based on one or more objectives. A method includes obtaining input data, wherein the input data includes at least one of: user data associated with one or more current charging station users, or non-user data that is not associated with the current charging station users. The method includes processing the input data through one or more machine learning engines, wherein the one or more machine learning engines are trained to determine a particular power distribution, among the plurality of charging station ports, that achieves one or more objectives. The method includes configuring the plurality of charging station ports according to the particular power distribution. The particular power distribution specifies a maximum charging rate or a percentage of a power budget for each of the plurality of charging station ports.

Abstract: A system and method are provided for automatically alerting drivers to potential tread ware problems to enable them to avoid the danger hazards associated with worn treads. A tread-evaluation station is placed at a location where images of tires may be captured. Images of tires are recorded when a vehicle is at or near the tread-evaluation station. An automated analysis is performed on the images. Based on the automated analysis, tires depicted in the captured images are classified into categories of wear. The automated analysis may include a detection component trained to detect tires in images, and a classification model trained to assign a classification to the wear status of the tires identified by the detection component. The tread-evaluation station may further be trained to predict when tires that do not currently need replacing will need replacing.

Abstract: Techniques are described herein for predicting content exposure that will result from installing a panel at a location at which no panel is currently installed. The location may include at least one electric vehicle charging station (EVCS) that includes an integrated or external panel for displaying content. The techniques involve training a machine learning engine based on information obtained about locations at which panels are already installed. The information used to train the machine learning engine includes, for each existing installation location: (a) features of the location, and (b) exposure data that has been generated for the location. When the machine learning engine has been trained, the trained machine learning engine predicts the content exposure for a location at which no panel has been installed based on the features of that location.

Abstract: An approach is provided for estimating electric vehicle (EV) charge needs among a population in a particular region. A method includes obtaining region-specific information associated with the particular region; wherein the region-specific information includes a number of EV drivers in the particular region. The method includes generating needs prediction data that indicates the EV charge needs among the population by applying the region-specific information to a mobility simulation of electrical vehicle drivers in the particular region; wherein the mobility simulation comprises a plurality of probability distribution functions. The method includes generating, based on the needs prediction data, on a display device of a computing device, a display that suggests a plurality of locations at which to place EV charging stations within the particular region to satisfy the estimated EV charge needs indicated in the needs prediction data.

Abstract: An approach is provided for estimating the optimal number and mixture of types of electric vehicle (EV) charging stations (EVCS) at one or more points of interest (POIs). A method includes generating, based on an EV adoption model, an EV adoption prediction. The method includes generating, based on a mobility simulation model, a driver-type prediction that predicts percentages of EV drivers qualifying for various EV driver types. The method includes generating, based on the EV adoption prediction, the driver-type prediction, and a visitation model, a visitation prediction that predicts how many EV drivers of each type of EV driver will visit the POI. The method includes determining and displaying, based on how many EV drivers of each type of EV driver is predicted to visit the POI, for each type of EV charging station of a plurality of types of EV charging stations, an optimal number of EVCS to install.

Abstract: An approach is provided for estimating electric vehicle (EV) charging station demand at specifics points of interest. A method includes determining a percentage of visitors to a point of interest (POI) that are electric vehicle (EV) drivers, a first percentage of the EV drivers qualifying as essential drivers, a second percentage of the EV drivers qualifying as opportunistic drivers. The method includes feeding input data to an inference engine, wherein the input data includes the above percentages, a number of visitors to the POI, and charge rates for the opportunistic and essential drivers. The inference engine generates output data regarding predicted EV charging demand for the POI, and generates a display based on the output data.

Abstract: Impedance-matched output driver circuits include a first totem pole driver stage and a second totem pole driver stage. The first totem pole driver stage includes at least one PMOS pull-up transistor and at least one NMOS pull-down transistor therein responsive to a first pull-up signal and a first pull-down signal, respectively. The second totem pole driver stage has at least one NMOS pull-up transistor and at least one PMOS pull-down transistor therein responsive to a second pull-up signal and second pull-down signal, respectively. The linearity of the output driver circuit is enhanced by including a first resistive element that extends between the first and second totem pole driver stages.