Systems and methods for detecting vehicle idling and determining classifications for the vehicle idling

A device, comprising:

• one or more processors configured to:
• receive road facing camera (RFC) video data and idling events data associated with a vehicle;
• receive traffic data associated with the vehicle,
• wherein the traffic data is received from a source external to the vehicle, and
• wherein the traffic data and the idling events data are received from different sources;
• determine that an idling event of the idling events data is an idling event trigger;
• selectively perform:
• processing the idling events data and the traffic data, based on the idling event being an idling event trigger and with a first machine learning model, to determine a first score for the idling event, based on determining that the RFC video data is not available, or
• processing the idling events data, the RFC video data, and the traffic data, based on the idling event being an idling event trigger and with a machine learning model, to determine a second score for the idling event, based on determining that the RFC video data is available;
• determine a classification for the idling event based on the first score or the second score, and a score threshold;
• and
• perform one or more actions based on the classification.

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

• one or more instructions that, when executed by one or more processors of a device, cause the device to:
• receive road facing camera (RFC) video data, driver facing camera (DFC) video data, and idling events data associated with a vehicle;
• receive traffic data associated with the vehicle,
• wherein the traffic data is received from a source external to the vehicle, and
• wherein the traffic data and the idling events data are received from different sources;
• determine that an idling event of the idling events data is an idling event trigger;
• selectively perform:
• processing the idling events data and the traffic data, based on the idling event being an idling event trigger and with a first machine learning model, to determine a first score for the idling event, based on determining that the RFC video data and the DFC video data are not available,
• performing, based on determining that the RFC video data and the DFC video data are available:
• processing the idling events data and the DFC video data, based on the idling event being an idling event trigger and with a first machine learning model, to determine a behavior of a driver of the vehicle;
• and
• processing the behavior of the driver, the RFC video data, and the traffic data, with a second machine learning model, to determine a second core for the idling event;
• determine a classification for the idling event based on the first score or the second score, and a score threshold;
• and
• perform one or more actions based on the classification.