Abstract: The following relates generally to providing virtual reality (VR) alerts to a driver of an autonomous vehicle. For example, a vehicle may be driving autonomously while the driver is watching a VR movie (e.g., on a pair of VR goggles); the driver may then receive a VR alert recommending that the driver take control of the vehicle (e.g., switch the vehicle from autonomous to manual mode). The following also relates to generating a VR feed for presenting real-time road conditions so that a user may preview a road segment. The following also relates to generating a VR feed corresponding to an event (e.g., a vehicle collision, a crime, a weather event, and/or a natural disaster).

Abstract: A data analytics computing device for generating refined risk models responsive to vehicle-based telematics data is configured to receive geolocation data and telematics data associated with a plurality of vehicles. The geolocation data is captured at a location sensor of a vehicle computing device and representative of a location of the vehicle, and the telematics data is captured at a plurality of sensors of at least one of the vehicle or the vehicle computing device and representative of movement of the vehicle. The plurality of vehicles is associated with a geographic area. The data analytics computing device is also configured to apply machine-learning techniques to the geolocation data and the telematics data to define standard driving behavior within the geographic area, and generate driving behavior risk models for the geographic area based upon the standard driving behavior.

Abstract: A vehicle routing system includes a vehicle routing and analytics (VRA) computing device, one or more databases, and one or more vehicles communicatively coupled to the VRA computing device. The VRA computing device is configured to generate an optimal route for a vehicle to travel that maximizes potential revenue for operation of the vehicle, the optimal route including a schedule of a plurality of tasks, and generate analytics associated with operation of the vehicle. The VRA computing device is further configured to provide a management hub software application accessible by vehicle users associated with vehicles, tasks sources, and other users.

Abstract: A vehicle routing system includes a vehicle routing and analytics (VRA) computing device, one or more databases, and one or more vehicles communicatively coupled to the VRA computing device. The VRA computing device is configured to generate an optimal route for a vehicle to travel that maximizes potential revenue for operation of the vehicle, the optimal route including a schedule of a plurality of tasks, and generate analytics associated with operation of the vehicle. The VRA computing device is further configured to provide a management hub software application accessible by vehicle users associated with vehicles, tasks sources, and other users.

Abstract: A matching computer system for electronically generating, matching, and providing online user profiles, and determining a sharing score between the online user profiles is provided. The matching computer system may be configured to generate online user profiles associated with users of the systems. The matching computer system may be also configured to calculate a base score based upon the generated online user profile. Each base score represents a level of trustworthiness of each respective user. The matching computer system may be further configured to determine a sharing score between users based upon the base scores. Each sharing score represents a level of matching between the online user profiles.

Abstract: A vehicle routing system includes a vehicle routing and analytics (VRA) computing device, one or more databases, and one or more vehicles communicatively coupled to the VRA computing device. The VRA computing device is configured to generate an optimal route for a vehicle to travel that maximizes potential revenue for operation of the vehicle, the optimal route including a schedule of a plurality of tasks, and generate analytics associated with operation of the vehicle. The VRA computing device is further configured to provide a management hub software application accessible by vehicle users associated with vehicles, tasks sources, and other users.

Abstract: A data analytics computing device for generating refined risk models responsive to vehicle-based telematics data is configured to receive geolocation data and telematics data associated with a plurality of vehicles. The geolocation data is captured at a location sensor of a vehicle computing device and representative of a location of the vehicle, and the telematics data is captured at a plurality of sensors of at least one of the vehicle or the vehicle computing device and representative of movement of the vehicle. The plurality of vehicles is associated with a geographic area. The data analytics computing device is also configured to apply machine-learning techniques to the geolocation data and the telematics data to define standard driving behavior within the geographic area, and generate driving behavior risk models for the geographic area based upon the standard driving behavior.

Abstract: A personalized insurance (“PI”) computing device for determining an optimal insurance product for a driver operating a vehicle for a transportation network company (“TNC”) during a period of increased demand includes at least one processor in communication with at least one memory. The processor is configured to: (i) receive, from a TNC, data indicating increased demand for transportation services, (ii) retrieve driver data that includes the driver history, (iii) generate an optimal pricing model for the driver based upon the increased demand and the driver data, (iv) execute the model to determine an optimal insurance product having characteristics reflecting at least one risk factor associated with the increased demand for transportation services and a risk profile determined from analyzing the driver data, and (v) transmit an offer to the driver to provide transportation services at an increased earnings rate and with the determined optimal insurance product.

Abstract: A method of facilitating first mile/last mile transfer of a vehicle includes: analyzing a first route and a second route using a trained machine learning model to determine, respectively, a first cost of the first route and a second cost of the second route to a user; wherein the trained machine learning model is trained using at least one of a detour in time, a delay in miles, a time of day, a traffic congestion factor, or profile information about a ride share passenger; causing a message including the first cost and the second cost to be displayed to the user via an electronic device of the user; and receiving an indication of whether the user selects the first route or the second route.

Abstract: The following relates generally to AI-based review of insurance claims complaints. In some embodiments, one or more processors: (1) receive, via a chatbot, an insurance claim complaint; (2) categorize, via the chatbot, the insurance claim complaint by determining a category of the insurance claim complaint, the category comprising a tone category or a policy category; (3) build, via the chatbot, a complaint report including information of the insurance claim complaint and an indication of the category; and/or (5) send, via the chatbot, the complaint report to an insurance complaint administrator computing device.

Abstract: The following relates generally to artificial intelligence (AI)-based responses to social media posts. In some embodiments, one or more processors may be configured to: (1) receive, via a chatbot, a social media post; (2) categorize, via the chatbot, the social media post; (3) determine, via the chatbot, based upon the categorization, an entity to contact; (4) build, via the chatbot, based upon the determined entity to contact, a response to the social media post; and/or (5) send, via the chatbot, the response to the entity.

Abstract: The following relates generally to artificial intelligence (AI)-based communication with an insurance customer. In some embodiments, one or more processors: receive, via a chatbot (or voicebot or other bot), a question in a first language; translate, via the chatbot, the question from the first language to a second language; determine, via the chatbot, an answer to the translated question, the answer being determined in the second language; translate, via the chatbot, the answer from the second language to the first language; and/or present, via the chatbot, the answer in the first language to an insurance customer.

Abstract: Methods and systems for charging an electric vehicle (EV) are described herein. An EV may require additional battery power to reach a charging station. A remote server in communication with the EV or an on-board computer or mobile device in the EV may obtain data to determine a location for the EV to meet a charging vehicle. The charging vehicle may be dispatched to meet the EV and deliver power to it, enabling the EV to reach a charging station or other destination. In some examples, the charging vehicle may deliver power to the EV while both vehicles are stationary. In other examples, the charging vehicle may couple to the EV while both vehicles are in motion.

Abstract: Methods and systems for alerting a user of crime-prone locations are disclosed herein. In some embodiments, the method comprises: (1) receiving, by one or more processors from a user device, location data of the user device and a destination input by the user; (2) determining, by the one or more processors, based upon the location data, that the user device is approaching the destination; (3) loading, by the one or more processors, a crime density map surrounding the destination; (4) determining, by the one or more processors, based upon the crime density map, at least one non-crime-prone parking spot, wherein the at least one non-crime-prone parking spot meets at least one predetermined criterion; and/or (5) presenting, by the one or more processors via the user device, an indication of the at least one non-crime-prone parking spot.

Abstract: Methods and systems for alerting a user of accident-prone locations are disclosed herein. In some embodiments, the method comprises: (1) receiving, by one or more processors and from a user device associated with the user, location data and motion data of the user device; (2) determining, by the one or more processors, based upon the motion data, that the user has started a trip; (3) loading, by the one or more processors, an accident density map surrounding a location of the user device based upon the location data; (4) determining, by the one or more processors, based upon the accident density map, at least one accident-prone location within a scope of the accident density map, wherein the at least one accident-prone location meets at least one predetermined criterion; and/or (5) presenting, by the one or more processors via the user device, an indication of the at least one accident-prone location.

Abstract: The following relates generally to providing virtual reality (VR) alerts to a driver of an autonomous vehicle. For example, a vehicle may be driving autonomously while the driver is watching a VR movie (e.g., on a pair of VR goggles); the driver may then receive a VR alert recommending that the driver take control of the vehicle (e.g., switch the vehicle from autonomous to manual mode). The following also relates to generating a VR feed for presenting real-time road conditions so that a user may preview a road segment. The following also relates to generating a VR feed corresponding to an event (e.g., a vehicle collision, a crime, a weather event, and/or a natural disaster).

Abstract: A matching computer system for electronically generating, matching, and providing online user profiles, and determining a sharing score between the online user profiles is provided. The matching computer system may be configured to generate online user profiles associated with users of the systems. The matching computer system may be also configured to calculate a base score based upon the generated online user profile. Each base score represents a level of trustworthiness of each respective user. The matching computer system may be further configured to determine a sharing score between users based upon the base scores. Each sharing score represents a level of matching between the online user profiles.

Abstract: Embodiments described herein receive telematics data collected over a period of time, wherein the telematics data is indicative of operation of a vehicle by an owner of the vehicle during the period of time; analyze the telematics data to identify driving behavior(s) of the owner during the period of time; predict one or more user preference values of a vehicle-sharing platform profile of the owner based on the identified one or more driving behaviors, wherein the one or more user preference values define one or more criteria for vehicle renters with whom the first vehicle can be shared; apply the one or more criteria to a potential vehicle renter; and cause an indication of the first vehicle of the owner to be displayed via a mobile device of the potential renter only if the potential vehicle renter satisfies the one or more criteria.

Abstract: Methods and systems for providing emergency heating in an electric vehicle (EV) running out of power are described herein. An on-board computer or mobile device in an EV may determine that an amount of charge remaining for powering the EV is below a threshold charge level. The on-board computer or mobile device may then route the remaining amount of charge to power a heating system in the EV to maintain a temperature in the EV above a threshold temperature level, and shut down power to other components within the EV.

Abstract: A personalized insurance (“PI”) computing device for determining an optimal insurance product for a driver operating a vehicle for a transportation network company (“TNC”) during a period of increased demand includes at least one processor in communication with at least one memory. The processor is configured to: (i) receive, from a TNC, data indicating increased demand for transportation services, (ii) retrieve driver data that includes the driver history, (iii) generate an optimal pricing model for the driver based upon the increased demand and the driver data, (iv) execute the model to determine an optimal insurance product having characteristics reflecting at least one risk factor associated with the increased demand for transportation services and a risk profile determined from analyzing the driver data, and (v) transmit an offer to the driver to provide transportation services at an increased earnings rate and with the determined optimal insurance product.