Abstract: A mechanism for interpreting spatiotemporal data from augmented reality devices and virtual reality devices based on an analysis performed by one or more predictive machines. The predictive machines may implement machine learning techniques and/or algorithms to predict a heart rate for a user, from which user calorie expense can be calculated. In operation, a virtual reality or augmented reality device can receive positional data over time, for example derived from image data from a camera. The positional data can be averaged over time, and the averages can be rolled-up, and the averaged and rolled-up data can be fed into a predictive machine to generate a heart rate prediction. The heart rate prediction is used to generate a user heart rate, from which calories for the user's corresponding motion is determined.

Abstract: Technologies for providing real-time visualizations of a behavior of an autonomous vehicle (AV) associated with a ride request. In some examples, a method for providing real-time visualizations of a behavior of an AV associated with a ride request can include receiving a user request for a ride from an AV, wherein the user request specifies a pick-up location associated with a user; receiving sensor data from one or more sensors associated with the AV; determining, based on the sensor data, a state and context of the AV while the AV is en route to the pick-up location; and presenting, at a display interface, a map depicting one or more visual indicators of the state and context of the AV, the state and context of the AV including a location of the AV and one or more AV operations.

Abstract: Technologies for providing real-time visualizations of a behavior of an autonomous vehicle (AV) associated with a ride request. In some examples, a method for providing real-time visualizations of a behavior of an AV associated with a ride request can include receiving a user request for a ride from an AV, wherein the user request specifies a pick-up location associated with a user; receiving sensor data from one or more sensors associated with the AV; determining, based on the sensor data, a state and context of the AV while the AV is en route to the pick-up location; and presenting, at a display interface, a map depicting one or more visual indicators of the state and context of the AV, the state and context of the AV including a location of the AV and one or more AV operations.

Abstract: A mechanism for interpreting spatiotemporal data from augmented reality devices and virtual reality devices based on an analysis performed by one or more predictive machines. The predictive machines may implement machine learning techniques and/or algorithms to predict a heart rate for a user, from which user calorie expense can be calculated. In operation, a virtual reality or augmented reality device can receive positional data over time, for example derived from image data from a camera. The positional data can be averaged over time, and the averages can be rolled-up, and the averaged and rolled-up data can be fed into a predictive machine to generate a heart rate prediction. The heart rate prediction is used to generate a user heart rate, from which calories for the user's corresponding motion is determined.

Abstract: Technologies for providing real-time visualizations of a behavior of an autonomous vehicle (AV) associated with a ride request. In some examples, a method for providing real-time visualizations of a behavior of an AV associated with a ride request can include receiving a user request for a ride from an AV, wherein the user request specifies a pick-up location associated with a user; receiving sensor data from one or more sensors associated with the AV; determining, based on the sensor data, a state and context of the AV while the AV is en route to the pick-up location; and presenting, at a display interface, a map depicting one or more visual indicators of the state and context of the AV, the state and context of the AV including a location of the AV and one or more AV operations.

Abstract: Technologies for providing real-time visualizations of a behavior of an autonomous vehicle (AV) associated with a ride request. In some examples, a method for providing real-time visualizations of a behavior of an AV associated with a ride request can include receiving a user request for a ride from an AV, wherein the user request specifies a pick-up location associated with a user; receiving data indicating a state and/or context of the AV while the AV is en route to the pick-up location; generating, based on the data indicating the state and/or context of the AV, one or more visual indicators of the state and/or context of the AV; and presenting, at a display interface, a map depicting the one or more visual indicators of the state and/or context of the AV, the state and/or context of the AV including a location of the AV and/or one or more AV operations.