Abstract: Method and system for providing carbon offsets. For example, the method includes collecting mindful driving data for one or more vehicle trips made by a user, analyzing the mindful driving data to determine a level of mindful driving of the user, determining a level of carbon offset reward based at least in part upon the level of mindful driving of the user, determining an amount of total carbon emission of the user, and providing an amount of carbon offset reward based at least in part upon the level of carbon offset reward and the amount of total carbon emission.

Abstract: Method and system for predicting fuel consumption efficiency. For example, the method includes collecting past user driving data for one or more past vehicle trips that have already been made by a user, analyzing the past user driving data to determine one or more past user driving features related to a past fuel consumption efficiency of the user, collecting information for one or more future vehicle trips that will be made by the user during a predetermined future period of time, and predicting a future fuel consumption efficiency of the user during the predetermined future period of time based at least in part upon the information for the one or more future vehicle trips and the determined one or more past user driving features.

Abstract: Method and system for selecting a vehicle route. For example, the method includes collecting information for a future vehicle trip that will be made by a user, generating multiple routes for the same future vehicle trip, predicting an amount of fuel consumption for each of the multiple routes based at least in part upon information for the route and user driving features of the user, and selecting a route from the multiple routes based at least in part upon the amounts of fuel consumption predicted for the user, where the selected route corresponds to a least amount among the amounts of fuel consumption predicted for the user.

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.

Abstract: A system and method for analyzing risk and providing risk mitigation instructions. The system receives analyzes sensor data and other data corresponding to a user to determine a test group. The system uses the test group to determine a risk score, and, subsequently, a risk mitigation strategy. Machine learning techniques are implemented to refine how the test group, risk score, and mitigation are each selected.

Abstract: Systems and methods of visualizing predicted driving risk are provided herein. Vehicle sensor data associated with a vehicle operator may be analyzed. Based on the analysis of the vehicle sensor data, one or more vehicle operation risks associated with the vehicle operator may be predicted. Each vehicle operation risk may be associated with a portion of a vehicle associated with the vehicle operator. Additionally, each vehicle operation risk may be assigned a priority level, e.g., based on predicted likelihood of occurrence, predicted danger of the vehicle operator, predicted damage to the vehicle, etc. A display overview of the vehicle may be presented to the vehicle operator. In the display overview of the vehicle portions of the vehicle associated with each of the predicted vehicle operation risks may be highlighted. The portions may be highlighted differently (using different colors, heavier/lighter shading, etc.) based on the priority level of their associated risks.

Abstract: Systems and methods for generating and communicating challenges that are intended to improve driving behavior are provided. According to certain aspects, a backend server may receive vehicle data describing the vehicle's operation or the vehicle operator from sensors or an electronic device. The backend server may compare target parameters and the vehicle data and, optionally, data from an external information source, to determine differences and calculate risk. The backend server may transmit challenges to the electronic device, and may generate a comparison factor and process an account of a the vehicle operator based on updated vehicle data received from the electronic device.

Abstract: Systems and methods of utilizing badging for driver risk assessment are provided. Vehicle telematics data (e.g., speed data, acceleration data, braking data, cornering data, following distance data, turn signal data, seatbelt use data) associated with a vehicle operator may be analyzed. Based on the analysis, a trend of safe operation of the vehicle may be identified (e.g., based on a number of vehicle trips and/or a number of consecutive vehicle trips in which the vehicle telematics data indicates safe operation of the vehicle, a frequency of vehicle trips in which the vehicle telematics data indicates safe operation of the vehicle, a comparison to other drivers, etc.) A digital certificate indicating the trend of safe operation of the vehicle may be associated with the vehicle operator. In response to a third-party query regarding the vehicle operator, the digital certificate associated with the vehicle operator may be displayed to the third party.

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.

Abstract: Systems and methods of efficient carpooling based on driver risk groups are provided. Vehicle operators may be classified into a carpooling group based on vehicle routes associated with each of the vehicle operators. Vehicle sensor data associated with each vehicle operator of the carpooling group may be analyzed. Based on the analysis of the vehicle sensor data, one or more indicia of safe driving behaviors associated with each vehicle operator of the carpooling group may be identified. The safe driving behaviors associated with each vehicle operator of the carpooling group may be compared. Based on the comparison, a vehicle operator of the carpooling group may be selected as a driver for a carpool. For example, the safest vehicle operator of the carpooling group may be selected as the driver for the carpool. The carpool may include one or more of the other vehicle operators of the carpooling group as passengers.

Abstract: Systems and methods of visualizing predicted driving risk are provided herein. Vehicle sensor data associated with a vehicle operator may be analyzed. Based on the analysis of the vehicle sensor data, one or more vehicle operation risks associated with the vehicle operator may be predicted. Each vehicle operation risk may be associated with a portion of a vehicle associated with the vehicle operator. Additionally, each vehicle operation risk may be assigned a priority level, e.g., based on predicted likelihood of occurrence, predicted danger to the vehicle operator, predicted damage to the vehicle, etc. A display overview of the vehicle may be presented to the vehicle operator. In the display overview of the vehicle portions of the vehicle associated with each of the predicted vehicle operation risks may be highlighted. The portions may be highlighted differently (using different colors, heavier/lighter shading, etc.) based on the priority level of their associated risks.

Abstract: A system and method for analyzing risk and providing risk mitigation instructions. The system receives analyzes sensor data and other data corresponding to a user to determine a test group. The system uses the test group to determine a risk score, and, subsequently, a risk mitigation strategy. Machine learning techniques are implemented to refine how the test group, risk score, and mitigation are each selected.

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.

Abstract: Systems and methods for reducing vehicle collisions based on driver risk groups are provided. A plurality of vehicle operators may be classified into a driver risk group based on one or more attributes (e.g., location, workplace, school, demographic, hobby, interest, etc.) shared by the plurality of vehicle operators. Vehicle sensor data (e.g., speed data, acceleration data, braking data, cornering data, following distance data, turn signal data, seatbelt use data, etc.) associated with each of the plurality of vehicle operators of the driver risk group may be analyzed. Based on the analysis of the vehicle sensor data, one or more indicia of safe driving behavior associated with the driver risk group may be identified. In response to a third-party query regarding a vehicle operator in the driver risk group, an indication of the safe driving behavior associated with the driver risk group may be provided to the third party.

Abstract: Systems and methods for providing continuous safe-driver training safely are provided. A safe-driving challenge may be presented to an operator of a vehicle. Data captured by sensors associated with the vehicle may be analyzed to determine whether the operator of the vehicle has completed the safe-driving challenge. Based on a determination that the operator of the vehicle has completed the safe-driving challenge, a notification may be provided to the operator (e.g., indicating to the operator that he or she has successfully completed the challenge). A processor may randomly select whether a reward is to be provided to the operator of the vehicle based on the determination that the operator of the vehicle has completed the safe-driving challenge. Moreover, if a reward is to be provided to the operator of the vehicle, a processor may randomly select a type of reward to be provided to the operator of the vehicle.

Abstract: Systems and methods of incentivizing the use of a safety device while operating a vehicle are provided. Data captured by sensors associated with a safety device may be analyzed to identify instances in which the safety device is used by an operator of a vehicle. Additionally, data captured by sensors associated with the vehicle may be analyzed to identify instances in which the vehicle is in motion. By comparing the instances in which the safety device is used by the operator of the vehicle to the instances in which the vehicle is in motion, instances in which the operator of the vehicle uses the safety device while operating the vehicle may be determined. Furthermore, a trend of the operator of the vehicle using the safety device while operating the vehicle may be identified. A notification related to the identified trend may be provided to the operator of the vehicle.

Abstract: Systems and methods of automatically controlling a user's data footprint are provided. Data associated with a user may be analyzed to determine an action the user is preparing to take. Based on the analysis, a potential risk associated with the action the user is preparing to take may be identified. The potential risk associated with the action the user is preparing to take may be, for example, a data security risk, a data privacy risk, a physical risk, a risk of damage to property, and/or a financial risk. A notification indicating the potential risk associated with the action the user is preparing to take may be provided to the user. The notification may include one or more suggestions for mitigating the potential risk associated with the action the user is preparing to take.

Abstract: Systems and methods of efficient carpooling based on driver risk groups are provided. Vehicle operators may be classified into a carpooling group based on vehicle routes associated with each of the vehicle operators. Vehicle sensor data associated with each vehicle operator of the carpooling group may be analyzed. Based on the analysis of the vehicle sensor data, one or more indicia of safe driving behaviors associated with each vehicle operator of the carpooling group may be identified. The safe driving behaviors associated with each vehicle operator of the carpooling group may be compared. Based on the comparison, a vehicle operator of the carpooling group may be selected as a driver for a carpool. For example, the safest vehicle operator of the carpooling group may be selected as the driver for the carpool. The carpool may include one or more of the other vehicle operators of the carpooling group as passengers.

Abstract: Systems and methods of incentivizing the use of a safety device while operating a vehicle are provided. Data captured by sensors associated with a safety device may be analyzed to identify instances in which the safety device is used by an operator of a vehicle. Additionally, data captured by sensors associated with the vehicle may be analyzed to identify instances in which the vehicle is in motion. By comparing the instances in which the safety device is used by the operator of the vehicle to the instances in which the vehicle is in motion, instances in which the operator of the vehicle uses the safety device while operating the vehicle may be determined. Furthermore, a trend of the operator of the vehicle using the safety device while operating the vehicle may be identified. A notification related to the identified trend may be provided to the operator of the vehicle.

Abstract: Systems and methods for reducing vehicle collisions based on driver risk groups are provided. A plurality of vehicle operators may be classified into a driver risk group based on one or more attributes (e.g., location, workplace, school, demographic, hobby, interest, etc.) shared by the plurality of vehicle operators. Vehicle sensor data (e.g., speed data, acceleration data, braking data, cornering data, following distance data, turn signal data, seatbelt use data, etc.) associated with each of the plurality of vehicle operators of the driver risk group may be analyzed. Based on the analysis of the vehicle sensor data, one or more indicia of safe driving behavior associated with the driver risk group may be identified. In response to a third-party query regarding a vehicle operator in the driver risk group, an indication of the safe driving behavior associated with the driver risk group may be provided to the third party.