Abstract: An interaction detection and analysis (“IDA”) computing device that includes at least one processor in communication with at least one memory device is provided. The at least one processor being configured to store software ecosystem data, environmental condition data, and performance data in a plurality of data records in a database, wherein each data record i) is associated with one autonomous vehicle (AV) of a plurality of A Vs and ii) includes the software ecosystem data, the environmental condition data, and the performance data of the corresponding AV; apply at least one machine learning algorithm to a set of the plurality of data records to identify an interaction between at least one software application and at least one environmental condition resulting in a particular outcome; and transmit, to at least one AV associated with the set of data records, at least one alert message advising of the particular outcome.

Abstract: An interaction detection and analysis (“IDA”) computing device that includes at least one processor in communication with at least one memory device is provided. The at least one processor being configured to store software ecosystem data, environmental condition data, and performance data in a plurality of data records in a database, wherein each data record i) is associated with one autonomous vehicle (AV) of a plurality of AVs and ii) includes the software ecosystem data, the environmental condition data, and the performance data of the corresponding AV; apply at least one machine learning algorithm to a set of the plurality of data records to identify an interaction between at least one software application and at least one environmental condition resulting in a particular outcome; and transmit, to at least one AV associated with the set of data records, at least one alert message advising of the particular outcome.

Abstract: A distracted driving analysis system for identifying distracted driving events is provided. The system includes a processor in communication with a memory device, and the processor is programmed to: (i) receive labeled training data, the labeled training data including driving event records (a) each labeled as an actual distracted driving event or a passenger event and (b) including phone usage by a user that occurred within a time period of a driving event, (ii) identify common features of the actual distracted driving events and the passenger events by processing the training data using a supervised machine learning algorithm, (iii) generate a trained model based at least in part upon the identified common features, (iv) process a new driving event, (v) assign the new driving event based at least in part upon features of the new driving event, and/or (vi) determine whether the new driving event is an actual distracted driving event or a passenger event.

Abstract: The present disclosure is directed to apparatuses, systems and methods for automatically classifying digital images of occupants inside a vehicle. More particularly, the present disclosure is directed to apparatuses, systems and methods for automatically classifying digital images of occupants inside a vehicle by comparing current image data to previously classified image data.

Abstract: A ring can include a housing. The housing can include a first power source configured to be charged by a second power source removably positioned adjacent to the housing. The housing also can include a component. The component can be configured to draw energy from the first power source. The component also can sense a physical phenomenon external to the housing. The component further can send communication signals to a communication device. Other embodiments are disclosed.

Abstract: A method can include detecting an amount of a force applied to a brake of a vehicle. The method can also include, after detecting the force applied to the brake of the vehicle, illuminating one or more light emitting devices of the vehicle in a manner indicative of the amount of the force detected to have been applied to the brake of the vehicle. Other embodiments are described.

Abstract: Method, system, device, and non-transitory computer-readable medium for adjusting a dynamic discount. In one aspect, a computer-implemented method includes: receiving an enrollment request from a user associated with a vehicle for a vehicle insurance policy; issuing an initial discount to the user for the vehicle insurance policy at a first time; collecting trip data associated with a set of vehicular trips operated by the user with the vehicle, the trip data including telematics data and travel condition data; and dynamically updating the initial discount based at least in part upon the trip data as trip data are collected.

Abstract: Method and system for determining which mobile device among multiple mobile devices is used by a vehicle driver. For example, the method includes receiving first telematics data and first device interaction data generated by a first mobile device, receiving second telematics data and second device interaction data generated by a second mobile device, analyzing the first telematics data and the first device interaction data to determine whether a first user is interacting with the first mobile device, analyzing the second telematics data and the second device interaction data to determine whether a second user is interacting with the second mobile device, determining whether the first user or the second user is the vehicle driver by using a classification technique, and calibrating the classification technique based on whether the first user or the second user has been determined to be the vehicle driver.

Abstract: An online system receives a request to confirm a transaction that is associated with an order. The system accepts or declines the transaction based on whether an amount associated with the pending transaction is likely to exceed an expected amount of the order by more than a threshold value. To determine the threshold, the system trains a first model to predict an overspend for an order and then trains a second model to predict an amount of error associated with the predictions from the first model. The outputs of the first model and the second model provide a mean and a variance for an expected distribution of the overspend. If the actual overspend amount for the transaction exists in too high of a percentile of the distribution, the transaction may be flagged for review or declined.

Abstract: A computer-implemented method includes: receiving a set of unlabeled telematics data; identifying a first set of baseline operation features; identifying a second set of baseline operation features; identifying a set of representative operation features associated with the unlabeled vehicular trip; generating a first set of baseline operation feature vectors, a second set of baseline operation feature vectors, and a set of representative operation feature vectors; classifying the unlabeled vehicular trip as a work trip when the set of representative operation feature vectors deviate from the first set of baseline operation feature vectors by less than a first deviation threshold; and classifying the unlabeled vehicular trip as a personal trip when the set of representative operation feature vectors deviates from the second set of baseline operation feature vectors by less than a second deviation threshold. Other embodiments are described.

Abstract: A method for implementing a machine learning model to predict risk exposure can include acquiring, via a sleep detecting device associated with a user, a set of user data. The method for implementing the machine learning model can also include predicting, by at least a trained ML model, a level of risk exposure for an other activity for the user. The trained ML model can be trained utilizing data indicative of one or more sleep patterns and data indicative of the other activity to identify one or more relationships between the one or more sleep patterns and the level of risk exposure for the other activity. The method can further include generating a notification to alert the user of the level of risk exposure, as predicted, for the other activity. Other embodiments are disclosed.

Abstract: A method can comprise receiving location data and movement data of the user. The method can further comprise identifying one or more points of interest (POIs) comprising one or more local businesses. The method can also comprise generating one or more local business metrics for the one or more local businesses. The method can further comprise generating a map overlayed with one or more indicators identifying one or more local business frequencies for the one or more local businesses visited. Additionally, the method can comprise transmitting, for display on an electronic device of the user, the one or more local business metrics and the map with the one or more indicators. Other embodiments are described.

Abstract: A computer-implemented method for training a virtual character, the computer-implemented method: receiving telematics data associated with one or more real trips; determining, based at least in part upon the telematics data, skill points associated with real skills detected during the one or more real trips; generating, based at least in part upon a virtual character profile for the virtual character, one or more virtual occurrences to be encountered by the virtual character; determining, based at least in part upon virtual ratings associated with the skill points and the virtual character profile, one or more virtual outcomes associated with the one or more virtual occurrences; updating the virtual character profile based at least in part upon the one or more virtual outcomes; and providing a reward based upon the one or more virtual outcomes. Other descriptions are included.

Abstract: Method and system for transforming telematics data for agnostic use. For example, a computer-implemented method includes: receiving, from a first telematics system of a first type, telematics data in a first form, the telematics data being associated with a vehicle; and transforming, via a prediction model, the telematics data into a first prediction in a second form, the second form being: different from the first form, and the form which data collected, processed, stored, or transmitted by a second telematics system of a second type would be, the second type being different from the first type.

Abstract: Method, system, device, and non-transitory computer-readable medium for predicting trip data. In one aspect, a computer-implemented method includes: obtaining a first set of trip data associated with a first set of vehicular trips operated by a vehicle operator during a first time period, the first set of trip data including a first set of telematics data and a first set of travel conditions; obtaining a second set of travel conditions associated with a second set of vehicular trips associated with a second time period separate from the first time period; and determining a second set of telematics data associated with a target vehicular trip based at least in part upon the first set of telematics data, the first set of travel conditions, and the second set of travel conditions.

Abstract: Method, system, device, and non-transitory computer-readable medium for data management. In some examples, a computer-implemented method can include: receiving personal data sets associated with vehicle operators; receiving, via sensors, sensor data sets associated with the vehicle operators, wherein the sensors comprise at least one of a Global Positioning Systems (GPS) sensor configured to provide a geographic location, an accelerometer configured to provide at least a linear or angular acceleration, or a gyroscope configured to provide orientation sensor data; receiving, from a requesting party of marketplace participants, an information request for a target operator profile associated with a target data profile selected from data profiles of the vehicle operators; and transmitting, in response to the information request, the target operator profile to the requesting party.

Abstract: A computer-implemented method of generating a route can include: identifying, by one or more processors, one or more indications of calmness associated with one or more route segments between an origin location and a destination location by analyzing sensor data associated with the one or more route segments; determining one or more calmness scores associated with the one or more route segments based upon the one or more indications of calmness, as identified, associated with the one or more route segments; and generating, by the one or more processors, a route between the origin location and the destination location, wherein the route is selected based at least in part upon the one or more calmness scores associated with the one or more route segments. Other embodiments are described.

Abstract: A computer-implemented method for operating a computing device including: receiving, by one or more processors and from one or more sensors, sensor data corresponding to a user; receiving, by the one or more processors, demographic data corresponding to the user; determining a pattern of behavior of the user based on the sensor data and the demographic data; and determining, using a trained risk analyzer machine learning model, a risk score of the user based at least in part upon a test group associated with the pattern of behavior of the user. Other descriptions are enclosed.

Abstract: Method and system for verifying a reliability of sensor data received from a mobile device of a user are disclosed. For example, the method includes receiving first sensor data collected and/or generated by one or more sensors of the mobile device from an application installed on a mobile device of a user at a first time, receiving second sensor data collected and/or generated by the one or more sensors of the mobile device from the application at a second time, determining whether the mobile device is in a vehicle that the user is driving during a time interval based at least upon the first sensor data and the second sensor data, and in response to the mobile device not being in the vehicle that the user is driving during the time interval, transmitting a notification to the mobile device indicating that the application does not work properly.

Abstract: Telematics systems and methods are described for generating interactive animated guided user interfaces (GUIs). A telematics cloud platform is configured to receive vehicular telematics data from a telematics device onboard a vehicle. A GUI value compression component determines, based on the vehicular telematics data, a plurality of GUI position values and a plurality of corresponding GUI time values. A geospatial animation app receives the plurality of GUI position values and the plurality of corresponding GUI time values. The geospatial animation app implements an interactive animated GUI that renders a plurality of geospatial graphics or graphical routes on a geographic area map via a display device. The geospatial graphics or graphical routes are rendered to have different visual forms based on differences between respective GUI position values and corresponding GUI time values.