| CPC G06Q 40/08 (2013.01) [G06F 18/251 (2023.01); G06N 20/00 (2019.01); G08G 1/0129 (2013.01)] | 15 Claims |
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1. A method, comprising:
obtaining, by a classification server system having a wireless network connection with a telematics device, telematics data comprising acceleration data, speed data, and heading data for a vehicle;
based on a determination that the telematics data indicates that an acceleration of the vehicle has exceeded a predefined g-force threshold in a predefined number of samples, increasing an upload frequency of the telematics data, wherein the telematics data indicating that the acceleration of the vehicle has exceeded the predefined g-force threshold in the predefined number of samples indicates that the vehicle has been involved in a vehicular accident, wherein the increased upload frequency of the telematics data causes an increased amount of the telematics data to be transmitted per each period of time of transmittal of the telematics data, wherein the telematics data includes data that occurred before the acceleration of the vehicle exceeded the predefined g-force threshold, data that occurred at the time the acceleration of the vehicle exceeded the predefined g-force threshold, and data that occurred after the acceleration of the vehicle exceeded the predefined g-force threshold;
obtaining, by the classification server system, the telematics data at the increased upload frequency;
generating, by the classification server system, common sampling rate data based on the telematics data, wherein the common sampling rate data comprises the acceleration data sampled at a first sampling rate using an accelerometer and location data sampled at a second sampling rate using a GPS receiver, the first sampling rate and the second sampling rate converted to a common sampling rate;
generating, by the classification server system and based on the common sampling rate data, a data set comprising a set of latent features associated with divided severity level buckets by:
training a machine-learning classifier to label the data set into a plurality of severity level buckets corresponding to at least a major severity label and a minor severity label by providing the machine-learning classifier:
historical accident outcome data including repair cost amount values; and
predefined cost amount thresholds which define the major severity label and the minor severity label;
obtaining, by the machine-learning classifier of the classification server system, accident predictions for the vehicle involved in the vehicular accident by classifying the data set based on historical telematics data and the historical accident outcome data, the historical telematics data and the historical accident outcome data having a predefined statistical similarity to the vehicular accident;
determining, by the classification server system, a decision for the vehicle based on the accident predictions;
based on the decision for the vehicle, automatically causing a tow truck to be dispatched to a location associated with the vehicle, with a target destination to which the tow truck is to tow the vehicle to;
calculating a predicted cost associated with the accident predictions by providing the machine-learning classifier the accident predictions for the vehicle; and
optimizing the machine-learning classifier by automatically updating a hyperparameter of the machine-learning classifier, based on the predicted cost associated with the accident predictions for the vehicle.
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