Abstract: Apparatuses, systems and methods are provided for generating a vehicle driver signature. More particularly, apparatuses, systems and methods are provided for generating a vehicle driver signature based on current image data, previously classified image data, and vehicle dynamics data.

Abstract: Apparatuses, systems and methods are provided for generating a vehicle driver signature. More particularly, apparatuses, systems and methods are provided for generating a vehicle driver signature based on vehicle interior image data and vehicle dynamics data. The vehicle interior image data may be representative of an identification of a particular individual. The vehicle dynamics data may be representative of a particular driving style, or driving behavior, associated with a particular individual.

Abstract: A method for attributing vehicle telematics data to individuals may include receiving vehicle telematics data collected by a data collection device during a plurality of trips. Subsets of the vehicle telematics data may correspond to different trips, and may be used to generate respective metric sets. Each metric set may include metrics indicative of different driving behaviors and/or different features of a driving environment. The method may also include retrieving, from a policy database, policy information pertaining to an insurance policy associated with the data collection device, and determining, based upon the policy information, a number of disclosed drivers associated with the insurance policy. A statistical analysis that includes executing a clustering algorithm may be performed on the metric sets, and, based upon the results, at least some of the metrics and/or at least some of the subsets of vehicle telematics data may be assigned to the disclosed drivers.

Abstract: Historical vehicle telematics data, corresponding to trips known to have been driven by specific persons within a limited pool of potential drivers, may be processed to generate a statistical model that is customized for those persons. Once generated, the custom statistical model may be used to process vehicle telematics data from trips where it is known that the driver was one of the drivers in the pool of drivers, but the specific identify of the driver is not known. Because the statistical model is specifically optimized or designed to distinguish among the drivers in the pool, the model may be more accurate than universal driver identification models.

Abstract: Systems and methods are described for the visualization of vehicular-based telematics data. In various aspects, telematics data may be aggregated for a plurality of vehicles where the telematics data can include telematics data observation(s) for each vehicle. Each observation can indicate a coordinate value of the vehicle and a timestamp for the observation, and can further indicate any of a device identifier for a telematics device associated with the vehicle, a speed value of the vehicle, a g-force value of the vehicle, a trip identifier associated with the vehicle, a distance value of the vehicle, or a stop indicator value of the vehicle. A visualization may also be generated based on at least a subset of the telematics data such that the visualization can indicate one or more image features associated with the one or more of the plurality of vehicles.

Abstract: A method for attributing vehicle telematics data to individuals may include receiving vehicle telematics data collected by a data collection device during a plurality of trips. Subsets of the vehicle telematics data may correspond to different trips, and may be used to generate respective metric sets. Each metric set may include one or more metrics each indicative of a different driving behavior or a different feature of a driving environment. The method may also include retrieving, from a policy database, policy information pertaining to an insurance policy associated with the data collection device, and determining, based upon the policy information, a number of disclosed drivers associated with the insurance policy. A statistical analysis may be performed on the metric sets, and, based upon the results, at least some of the metrics and/or at least some of the subsets of vehicle telematics data may be assigned to the disclosed drivers.

Abstract: Historical vehicle telematics data, corresponding to trips known to have been driven by specific persons within a limited pool of potential drivers, may be processed to generate a statistical model that is customized for those persons. Once generated, the custom statistical model may be used to process vehicle telematics data from trips where it is known that the driver was one of the drivers in the pool of drivers, but the specific identify of the driver is not known. Because the statistical model is specifically optimized or designed to distinguish among the drivers in the pool, the model may be more accurate than universal driver identification models.