Abstract: Methods, computer-readable media, software, and apparatuses may determine, based upon edge-computing operations, that a vehicular trip has been initiated and cause one or more sensors to collect vehicle data. One or more trip segments for at least a portion of the vehicular trip may be determined. In some aspects, for each trip segment, a first plurality of time features and a second plurality of frequency features may be determined, and may be concatenated with a third plurality of GPS features to form a feature vector. An accuracy measure may be determined based on the feature vector, and a mode for the vehicle may be predicted.

Abstract: Methods, computer-readable media, software, and apparatuses may determine, based upon edge-computing operations, that a vehicular trip has been initiated and cause one or more sensors to collect vehicle data. One or more trip segments for at least a portion of the vehicular trip may be determined. In some aspects, for each trip segment, a first plurality of time features and a second plurality of frequency features may be determined, and may be concatenated with a third plurality of GPS features to form a feature vector. An accuracy measure may be determined based on the feature vector, and a mode for the vehicle may be predicted.

Abstract: Methods, computer-readable media, software, and apparatuses may determine, based upon edge-computing operations, that a vehicular trip has been initiated and cause one or more sensors to collect vehicle data. One or more trip segments for at least a portion of the vehicular trip may be determined. In some aspects, for each trip segment, a first plurality of time features and a second plurality of frequency features may be determined, and may be concatenated with a third plurality of GPS features to form a feature vector. An accuracy measure may be determined based on the feature vector, and a mode for the vehicle may be predicted.

Abstract: Systems and methods in accordance with embodiments of the invention can obtain and use a variety of telematics and other data to predict potential claim or accident outcomes arising from a variety of incidents, such as automobile accidents. The claim or accident outcomes, such as parts of the vehicle damaged, accident category, expected liabilities, and many others, can be predicted based on the telematics and other data, such as accelerometer data, heading data, location/GPS, barometer, gyroscope, magnetometer data, and the like. The machine learning classifiers that can be generated are trained on historical data, consisting of claims, telematics, and/or other relevant data. In a variety of embodiments, the telematics data can be captured using a telematics device installed in the vehicle and/or via a mobile device associated with the vehicle.