Abstract: Electric vehicle battery monitoring systems and methods are disclosed herein. An example method includes displaying a state of charge for a battery of an electric vehicle on a display screen of the electric vehicle, determining a frequency of how often a user views the state of charge, inferring that the user has anxiety over a range for the electric vehicle based on the frequency, and presenting the user with options to extend the state of charge.

Abstract: A method for controlling a vehicle includes identifying a user identity associated with a user riding in the vehicle, determining a destination associated with the user identity, creating a detection zone proximate to the vehicle when the vehicle is localized in a first drop-off location associated with the destination, causing a sensory system to identify a hazard within the detection zone and generate a localization of the hazard, determining that a first probability of realizing a risk associated with the hazard is less than a first probability threshold, and generating a vehicle door actuation that permits the user to exit the vehicle based on the first probability of realizing the risk.

Abstract: A computer includes a processor and a memory storing instructions executable by the processor to receive a series of pressure maps indicating a respective series of sitting positions of an occupant in a seat, wherein the pressure maps include a current pressure map; update a profile of the occupant based on the pressure maps, wherein the profile includes a plurality of clusters of sitting positions and classifications of the clusters as preferred or nonpreferred, and updating the profile includes sorting one of the sitting positions into one of the clusters that is classified as preferred in response to the occupant remaining in that sitting position for greater than a threshold time; and adjust a physical configuration of the seat in response to the current sitting position being in one of the clusters that is classified as nonpreferred.

Abstract: A method for controlling a vehicle includes identifying a user identity associated with a user riding in the vehicle, determining a destination associated with the user identity, creating a detection zone proximate to the vehicle when the vehicle is localized in a first drop-off location associated with the destination, causing a sensory system to identify a hazard within the detection zone and generate a localization of the hazard, determining that a first probability of realizing a risk associated with the hazard is less than a first probability threshold, and generating a vehicle door actuation that permits the user to exit the vehicle based on the first probability of realizing the risk.

Abstract: An identifier for first set of display content on a vehicle display is input to a statistical model that outputs a plurality of probabilities that a user input will select each of a plurality of second set of display contents for display after the first set of display content is displayed. A first probability is identified for a predicted set of display content that is a highest probability in the plurality of probabilities. The plurality of probabilities are provided to at least one of an optimization model and a neural network upon determining an accuracy of the statistical model is below a threshold. Upon receiving, from the at least one of the optimization model and the neural network, a second probability for the predicted set of display content, the display content is selected based on the first and second probabilities. The vehicle display is updated based on the selected set of display content.

Abstract: A computer includes a processor and a memory storing instructions executable by the processor to receive a series of pressure maps indicating a respective series of sitting positions of an occupant in a seat, wherein the pressure maps include a current pressure map; update a profile of the occupant based on the pressure maps, wherein the profile includes a plurality of clusters of sitting positions and classifications of the clusters as preferred or nonpreferred, and updating the profile includes sorting one of the sitting positions into one of the clusters that is classified as preferred in response to the occupant remaining in that sitting position for greater than a threshold time; and adjust a physical configuration of the seat in response to the current sitting position being in one of the clusters that is classified as nonpreferred.

Abstract: Example embodiments described herein are generally directed to a navigation system that generates a pedestrian travel path based on various types of data stored in a database, such as, for example, a map, a blueprint, a historical record, a public record, and/or a personal record. In an example embodiment, the data may be obtained by executing a crowdsourcing procedure, where multiple individuals provide various types of information such as, information about an underground walkway, an elevated walkway, an unpaved pedestrian path, weather conditions, handicap-friendly pathways, and handicap-friendly structures. In an example application, a navigation device receives a navigation request that includes a start spot and an end spot for a pedestrian trip. The navigation device evaluates data stored in a database and uses the data to generate a pedestrian travel path that may include an underground walkway, an elevated walkway, an unpaved pedestrian path, and/or an enclosed pedestrian path.

Abstract: Parameters of clusters of a context-encoded model are updated, per frequency estimation of an aspect of calls to contacts with respect to unique combinations of contextual data of calls matching the respective clusters. The clusters are weighted according to relevance of the unique combinations of contextual data to current contextual information. First probabilities of calling individual contacts are determined according to the current contextual information and one or more inferences between calls determined from the clusters as weighted. Second probabilities of calling each of the plurality of contacts are determined according to a set of rules defining prioritizations for contacts based on external factors. Third probabilities of calling each of the plurality of contacts are determined according to calling patterns of other of the plurality of contacts. The first, second, and third sets of weights are combined to determine final predictions for predicted probabilities to call each of the contacts.