Automatic classification of inactive vehicle tracking unit

A device, comprising:

• one or more memories;
• and
• one or more processors, communicatively coupled to the one or more memories, to:
• receive message data associated with a vehicle tracking unit (VTU);
• identify, based on the message data, the VTU as an inactive VTU;
• compute a feature vector, associated with the VTU, based on the message data and environmental data associated with the VTU,
• wherein the feature vector is computed based on identifying the VTU as an inactive VTU, and
• wherein the environmental data includes information associated with at least one of:
• a point of interest included in an environment of the VTU, or
• location service coverage conditions in the environment of the VTU;
• determine, using an inactivity classification model, an inactivity classification associated with the VTU,
• wherein the inactivity classification is determined based on providing the feature vector, associated with the VTU, as an input to the inactivity classification model,
• wherein the inactivity classification indicates that the VTU has been classified as an anomalous non-reporting unit or that the VTU has been classified as a normal inactive unit, and
• wherein the inactivity classification model comprises a machine learning model trained, using historical feature vectors associated with respective historically inactive VTUs, to:
• receive, as input, the feature vector, and
• provide, as output, the inactivity classification;
• and
• cause an inactivity action to be performed based on the inactivity classification.

A non-transitory computer-readable medium storing instructions, the instructions comprising:

• one or more instructions that, when executed by one or more processors, cause the one or more processors to:
• receive message data associated with a vehicle tracking unit (VTU);
• identify, based on the message data, the VTU as an inactive VTU;
• compute a feature vector, associated with the VTU, based on the message data and environmental data associated with the VTU,
• wherein the feature vector is computed based on identifying the VTU as an inactive VTU, and
• wherein the environmental data includes information associated with at least one of:
• a point of interest included in an environment of the VTU, or
• location service coverage conditions in the environment of the VTU;
• determine, using an inactivity classification model, an inactivity classification associated with the VTU,
• wherein the inactivity classification is determined based on providing the feature vector, associated with the VTU, as an input to the inactivity classification model,
• wherein the inactivity classification indicates that the VTU has been classified as an anomalous non-reporting unit or that the VTU has been classified as a normal inactive unit, and
• wherein the inactivity classification model comprises a machine learning model trained, using historical feature vectors associated with respective historically inactive VTUs, to:
• receive, as input, the feature vector, and
• provide, as output, the inactivity classification;
• and
• cause an inactivity action to be performed based on the inactivity classification.