Virtual tagging of vehicles

A system comprising:

• one or more processors;
• and
• a non-transitory computer-readable medium storing instructions which, when executed by the one or more processors, configure the system to:
• receive a first set of measurements from one or more sensors of a mobile device while the mobile device is positioned in a first vehicle during a first drive;
• receive a second set of measurements from the one or more sensors of the mobile device while the mobile device is positioned in a vehicle during a second drive that occurs after completion of the first drive, wherein a sampling rate used by at least one sensor of the one or more sensors to collect the second set of measurements is a first sampling rate;
• generate, by a machine-learning model based on first set of measurements and the second set of measurements, an output indicating that the vehicle used during the second drive is a different vehicle than the first vehicle used during the first drive;
• and
• increase the sampling rate of the at least one sensor to be a second sampling rate that is higher than the first sampling rate for a remainder of the second drive based on the output from the machine-learning model.

A non-transitory computer-readable medium storing instructions which, when executed by one or more processors, configure the one or more processors to perform operations comprising:

• receiving a first set of measurements from one or more sensors of a mobile device while the mobile device is positioned in a first vehicle during a first drive;
• receiving a second set of measurements from the one or more sensors of the mobile device while the mobile device is positioned in a vehicle during a second drive that occurs after completion of the first drive, wherein a sampling rate used by at least one sensor of the one or more sensors to collect the second set of measurements is a first sampling rate;
• generating, by a machine-learning model based on the first set of measurements and the second set of measurements, an output indicating that the vehicle used during the second drive is a different vehicle than the first vehicle used during the first drive;
• and
• increasing the sampling rate of the at least one sensor to be a second sampling rate that is higher than the first sampling rate for a remainder of the second drive based on the output from the machine-learning model.