Abstract: Systems and methods for detecting and removing erroneous tire pressure values are provided. The method involves operating at least one processor to: receive tire pressure data comprising a time series of tire pressure values; simplify the tire pressure data by removing at least some of the tire pressure values that satisfy a predetermined interpolation error threshold; detect, using a trained machine learning classifier, at least one erroneous tire pressure value in the tire pressure data, each erroneous tire pressure value being detected based on the erroneous tire pressure value and a plurality of lagging tire pressure values consecutively trailing the erroneous tire pressure value; clean the tire pressure data by removing the at least one erroneous tire pressure value; and transmit the tire pressure data to a fleet management system, whereby the at least one erroneous tire pressure value is not transmitted to the fleet management system.

Abstract: Described herein are systems and methods for identifying a diagnostic trouble code (DTC) cycle for a vehicle. In an embodiment, one such method may comprise operating at least one processor to: receive telematics data originating from a plurality of telematics devices installed in a plurality of vehicles; identify, using the telematics data, a DTC generated by one or more of the plurality of vehicles, each DTC having a DTC status associated therewith; determine, for each DTC, the ignition cycle during which the DTC was generated; identify, for each DTC, a DTC status cycle by merging consecutive ignition cycles during which the DTC is generated with a consistent DTC status associated therewith; and identify, for each DTC, a DTC cycle based at least in part on each DTC status cycle identified for the DTC.

Abstract: Disclosed herein are systems and methods for identifying service centers. One of such methods may comprise operating at least one processor to: receive telematics data originating from a plurality of telematics devices installed in a plurality of vehicles; determine for each of the plurality of vehicles, using the telematics data, when a vehicle maintenance event has occurred by identifying at least a binary vehicle status indicator change, time-logged telematics data that meets a predetermined condition, an indication that a vehicle diagnosis has occurred, or a combination thereof; determine, using the telematics data, a location of each of the plurality of vehicles at a time at which the vehicle maintenance event occurred; and identify one or more service centers by: applying to a plurality of maintenance event locations a clustering model, and classifying one or more clusters of maintenance event locations as a service center.

Abstract: Disclosed herein are systems and methods for estimating remaining useful life of vehicle engine oil.

Abstract: Systems and methods for detecting and removing erroneous tire pressure values are provided. The method involves operating at least one processor to: receive tire pressure data comprising a time series of tire pressure values; simplify the tire pressure data by removing at least some of the tire pressure values that satisfy a predetermined interpolation error threshold; detect, using a trained machine learning classifier, at least one erroneous tire pressure value in the tire pressure data, each erroneous tire pressure value being detected based on the erroneous tire pressure value and a plurality of lagging tire pressure values consecutively trailing the erroneous tire pressure value; clean the tire pressure data by removing the at least one erroneous tire pressure value; and transmit the tire pressure data to a fleet management system, whereby the at least one erroneous tire pressure value is not transmitted to the fleet management system.

Abstract: Systems and methods for detecting and removing erroneous tire pressure values are provided. The method involves operating at least one processor to: receive tire pressure data comprising a time series of tire pressure values; simplify the tire pressure data by removing at least some of the tire pressure values that satisfy a predetermined interpolation error threshold; detect, using a trained machine learning classifier, at least one erroneous tire pressure value in the tire pressure data, each erroneous tire pressure value being detected based on the erroneous tire pressure value and a plurality of lagging tire pressure values consecutively trailing the erroneous tire pressure value; clean the tire pressure data by removing the at least one erroneous tire pressure value; and transmit the tire pressure data to a fleet management system, whereby the at least one erroneous tire pressure value is not transmitted to the fleet management system.

Abstract: Systems and methods for detecting and removing erroneous tire pressure values are provided. The method involves operating at least one processor to: receive tire pressure data comprising a time series of tire pressure values; simplify the tire pressure data by removing at least some of the tire pressure values that satisfy a predetermined interpolation error threshold; detect, using a trained machine learning classifier, at least one erroneous tire pressure value in the tire pressure data, each erroneous tire pressure value being detected based on the erroneous tire pressure value and a plurality of lagging tire pressure values consecutively trailing the erroneous tire pressure value; clean the tire pressure data by removing the at least one erroneous tire pressure value; and transmit the tire pressure data to a fleet management system, whereby the at least one erroneous tire pressure value is not transmitted to the fleet management system.