Abstract: Methods, computer-readable media, systems, and/or apparatuses for evaluating movement data to identify a user as a driver or non-driver passenger are provided. In some examples, movement data may be received from a mobile device of a user. The movement data may include sensor data including location data, such as global positioning system (GPS) data, accelerometer and/or gyroscope data, and the like. Additional data may be retrieved from one or more other sources. For instance, additional data such as usage of applications on the mobile device, public transportation schedules and routes, image data, vehicle operation data, and the like, may be received and analyzed with the movement data to determine whether the user of the mobile device was a driver or non-driver passenger of the vehicle. Based on the determination, the data may be deleted in some examples or may be further processed to generate one or more outputs.

Abstract: Aspects of the present disclosure describe systems, methods, and devices for automated vehicular control based on glare detected by an optical system of a vehicle. In some aspects, automated control includes controlling the operation of the vehicle itself, a vehicle subsystem, or a vehicle component based on a level of glare detected. According to some examples, controlling the operation of a vehicle includes instructing an automatically or manually operated vehicle to traverse a selected route based on levels of glare detected or expected along potentials routes to a destination. According to other examples, controlling operation of a vehicle subsystem or a vehicle component includes triggering automated responses by the subsystem or the component based on a level of glare detected or expected. In some additional aspects, glare data is shared between individual vehicles and with a remote data processing system for further analysis and action.

Abstract: Methods, computer-readable media, systems and apparatuses for determining a blind corner navigational score based on real-time or near real-time navigational analysis using sensor data, digital image data, and a map database are discussed. In some arrangements, detection of a blind sensor may be performing using sensor data, digital image data, and navigational data from a map database system. In at least some arrangements, a warning signal or a vehicle control signal may be transmitted to a vehicle in response to a determination that the blind corner navigational score is above a threshold. In at least some arrangements, route correction and/or route modification based on an upcoming blind corner may be performed if a blind corner navigational score is above a threshold.

Abstract: A system may be configured to detect an emergency condition at a premises; dispatch one or more autonomous drones to a location associated with the emergency condition; receive from the one or more autonomous drones, sensor data associated with the emergency condition; generate, based on the sensor data, a plan identifying an evacuation route for safely evacuating the premises; and transmit an instruction that causes the one or more autonomous drones to indicate, to one or more persons in a vicinity of the emergency condition, the evacuation route. The system may further detect, based on the sensor data, one or more safe areas in the premises, and the evacuation route may pass through at least one of the one or more safe areas.

Abstract: A system including a processor and memory may provide for automatically activating or deactivating a feature of a fleet vehicle. For example, one or more fleet vehicles may include one or more of a global-positioning system, a speed governor, electronically-controlled brakes, an electronically-controlled accelerator, a speed limiter, or an on-board computer with a processor and memory. One or more features may be activated by a local or remote computing device or system. For example, a system may determine one or more recommended routes between two or more locations. The system may track a fleet vehicle's progress along a route, and activate a feature of the fleet vehicle based on the fleet vehicle following or not following the recommended route. For example, the system may cause activation of a speed limiter on the fleet vehicle, disable the fleet vehicle, and/or activate or deactivate autonomous features of the fleet vehicle.

Abstract: Aspects of the disclosure relate to computing platforms that utilize improved mitigation analysis and policy management techniques to improve onboarding security. A computing platform may determine that a predetermined period of time has elapsed since finalizing an onboarding process. The computing platform may receive spot check verification inputs indicative of a user identity and may direct a mitigation analysis and output generation platform to analyze the spot check verification inputs. The computing platform may receive an indication of a correlation between the spot check verification inputs and expected spot check verification inputs. In response to determining that the correlation exceeds a predetermined threshold, the computing platform may determine that an additional verification test should be conducted, and may direct the mobile device to display an interface that prompts for additional onboarding verification inputs.

Abstract: A device may obtain data associated with an event and process the data via an evacuation prediction and management engine to obtain an evacuation prediction. When the evacuation prediction indicates that an evacuation order is likely to be issued within a period of time based on the event occurring, the device transmits, via the evacuation prediction and management engine, an evacuation service offering to a user device. Upon a user of the user device confirming acceptance of the evacuation service offering, the device receives a payment for the user for the evacuation service offering. When the event or evacuation order occurs and based on the payment for the evacuation service offering, the device automatically initiates a payment to a user account.

Abstract: Implementations described herein provide systems and methods for mitigating climate risk. In one implementation, an environmental forecast factor is determined based on one or more predictive climate models. The environmental forecast factor can be, for example, an environmental condition applied to a specific home. A severity of the environmental forecast factor on the specific home can be determined over a period of time. A financial instrument can be created that invests in an asset that is inversely related to the environmental forecast factor over the period of time, where the financial instrument is predicted to appreciate in value to at least the severity of the environmental forecast factor.

Abstract: Implementations described herein provide systems, methods, and devices for greenhouse gas monitoring and control. The systems, methods, and devices include a device detection system which identifies device identifier(s) corresponding to device(s) associated with a user. The device(s) include at least one of an electric vehicle, a home device, a power system device, or a power system device. Upon detecting the device(s), a user behavior analyzer receives energy consumption-related data from the device(s) resulting from energy consumption behavior of the user. The system determines, based on the energy consumption-related data, one or more behavior metrics of the user. A reduction integration generator identifies an energy consumption reduction integration corresponding to the one or more behavior metrics (e.g.

Abstract: Methods, computer-readable media, software, and apparatuses provide a system that may facilitate communications between drivers who share a route. The system may allow communications to be sent from one driver to another driver and allow drivers to post queries to other drivers sharing a route. Computing devices in the vehicle may collect route data for the system to evaluate and to use in identifying drivers sharing a route.

Abstract: Systems, methods, computer-readable media, and apparatuses for identifying and executing one or more interactive condition evaluation tests to generate an output are provided. In some examples, user information may be received by a system and one or more interactive condition evaluation tests may be identified. An instruction may be transmitted to a computing device of a user and executed on the computing device to enable functionality of one or more sensors that may be used in the identified tests. A user interface may be generated including instructions for executing the identified tests. Upon initiating a test, data may be collected from one or more sensors in the computing device. The data collected may be transmitted to the system and may be processed using one or more machine learning datasets to generate an output.

Abstract: Methods, apparatuses, systems, and computer-readable media for identifying and executing one or more interactive condition evaluation tests and collecting and analyzing user behavior data to generate an output are provided. In some examples, user information may be received and one or more interactive condition evaluation tests may be identified. An instruction may be transmitted to a computing device of a user and executed on the computing device to enable functionality of one or more sensors that may be used in the identified tests. Upon initiating a test, data may be collected from the one or more sensors. The collected sensor data may be transmitted to the system and processed using one or more machine learning datasets. Additionally, user behavior data may be collected and processed using one or more machine learning datasets. The sensor data, the user behavior data, and other data may be used together to generate an output.

Abstract: Methods, computer-readable media, systems, and/or apparatuses are provided for providing offer and insight generation functions. User input requesting an offer or insight may be received and an image of a photographic identification of a user may be requested. The image of the photographic identification may be captured and stored. A self-captured image of the user may be captured (e.g., via an image capture device of the computing device) and compared to an image of a user from the photographic identification. Responsive to determining that the images match, displaying an instruction to capture a vehicle identification number. The vehicle identification number may be captured. Data, including location data, may be extracted and an archive including the extracted data may be generated and the data may be transmitted to an entity computing system for processing. The entity computing system may evaluate the data and generate one or more insights and/or outputs.

Abstract: Systems and apparatuses for receiving data from a plurality of sensors and using the data, as well as other data, to generate a parking recommendation for an autonomous vehicle and instruct the autonomous vehicle to travel to the recommended parking location are provided. Data may be received from a plurality of sensors within a first autonomous vehicle, as well as from other vehicles and/or structures. Historical parking data associated with the first autonomous vehicle may also be extracted. In some examples, an expected future trip of the first autonomous vehicle may be determined. The system may then evaluate the data to generate a parking recommendation for the first autonomous vehicle. The system may generate and transmit instructions for traveling from a current location to the recommended parking location and may cause the first autonomous vehicle to travel to the recommended parking location.

Abstract: Methods, computer-readable media, software, and apparatuses may monitor consumer information in order to determine a probability of a data breach associated with a customer based on an online presence of the customer. The probability of a data breach may be used to present metrics to a consumer and/or a service provider. Further, the consumer may be presented with information indicating what factors contribute to the probability of a data breach, as well as information regarding how to reduce those factors.

Abstract: Aspects of the present disclosure describe systems, methods, and devices for automated vehicular control based on glare detected by an optical system of a vehicle. In some aspects, automated control includes controlling the operation of the vehicle itself, a vehicle subsystem, or a vehicle component based on a level of glare detected. According to some examples, controlling the operation of a vehicle includes instructing an automatically or manually operated vehicle to traverse a selected route based on levels of glare detected or expected along potentials routes to a destination. According to other examples, controlling operation of a vehicle subsystem or a vehicle component includes triggering automated responses by the subsystem or the component based on a level of glare detected or expected. In some additional aspects, glare data is shared between individual vehicles and with a remote data processing system for further analysis and action.

Abstract: Aspects of the disclosure relate to computing platforms that utilize improved mitigation analysis and policy management techniques to improve onboarding security. A computing platform may determine that a predetermined period of time has elapsed since finalizing an onboarding process. The computing platform may receive spot check verification inputs indicative of a user identity and may direct a mitigation analysis and output generation platform to analyze the spot check verification inputs. The computing platform may receive an indication of a correlation between the spot check verification inputs and expected spot check verification inputs. In response to determining that the correlation exceeds a predetermined threshold, the computing platform may determine that an additional verification test should be conducted, and may direct the mobile device to display an interface that prompts for additional onboarding verification inputs.

Abstract: Methods, computer-readable media, systems, and/or apparatuses are provided for providing offer and insight generation functions. User input requesting an offer or insight may be received and an image of a photographic identification of a user may be requested. The image of the photographic identification may be captured and stored. A self-captured image of the user may be captured (e.g., via an image capture device of the computing device) and compared to an image of a user from the photographic identification. Responsive to determining that the images match, displaying an instruction to capture a vehicle identification number. The vehicle identification number may be captured. Data, including location data, may be extracted and an archive including the extracted data may be generated and the data may be transmitted to an entity computing system for processing. The entity computing system may evaluate the data and generate one or more insights and/or outputs.

Abstract: Methods, apparatuses, systems, and computer-readable media for identifying and executing one or more interactive condition evaluation tests and collecting and analyzing user behavior data to generate an output are provided. In some examples, user information may be received and one or more interactive condition evaluation tests may be identified. An instruction may be transmitted to a computing device of a user and executed on the computing device to enable functionality of one or more sensors that may be used in the identified tests. Upon initiating a test, data may be collected from the one or more sensors. The collected sensor data may be transmitted to the system and processed using one or more machine learning datasets. Additionally, user behavior data may be collected and processed using one or more machine learning datasets. The sensor data, the user behavior data, and other data may be used together to generate an output.

Abstract: Systems, methods, computer-readable media, and apparatuses for identifying and executing one or more interactive condition evaluation tests to generate an output are provided. In some examples, user information may be received by a system and one or more interactive condition evaluation tests may be identified. An instruction may be transmitted to a computing device of a user and executed on the computing device to enable functionality of one or more sensors that may be used in the identified tests. A user interface may be generated including instructions for executing the identified tests. Upon initiating a test, data may be collected from one or more sensors in the computing device. The data collected may be transmitted to the system and may be processed using one or more machine learning datasets to generate an output.