Refining event triggers using machine learning model feedback

A vehicle device comprising:

• a computer readable storage medium having program instructions embodied therewith;
• and
• one or more processors configured to execute the program instructions to cause the vehicle device to:
• obtain sensor data associated with an image of a scene, the sensor data including one or more objects in the image;
• route the sensor data to a receiving server system using a network connection with the receiving server system, wherein the receiving server system comprises a first neural network, wherein the first neural network is configured to output a first probability of an event based on a first probability of a feature of the one or more objects;
• access a second neural network, wherein the second neural network is configured to output a second probability of the event based on a second probability of the feature of the one or more objects;
• identify the event based at least in part on the second probability of the event;
• obtain, from the receiving server system, a comparison of the first probability of the event and the second probability of the event;
• and
• determine one or more operations to dynamically adjust how the vehicle device identifies the event based on the comparison of the first probability of the event and the second probability of the event.

A computer-implemented method comprising:

• obtaining sensor data associated with an image of a scene, the sensor data including one or more objects in the image;
• routing the sensor data to a receiving server system using a network connection with the receiving server system, wherein the receiving server system comprises a first neural network, wherein the first neural network is configured to output a first probability of an event based on a first probability of a feature of the one or more objects in the image;
• accessing a second neural network, wherein the second neural network is configured to output a second probability of the event based on a second probability of the feature of the one or more objects in the image;
• identifying the event based at least in part on the second probability of the event;
• obtaining, from the receiving server system, a comparison of the first probability of the event and the second probability of the event;
• determining one or more operations to dynamically adjust how events are identified based on the comparison of the first probability of the event and the second probability of the event;
• implementing the one or more operations without retraining the second neural network;
• obtaining additional sensor data associated with an additional image of an additional scene, the additional sensor data identifying one or more additional objects of the additional image;
• accessing the second neural network, wherein the second neural network is further configured to output a third probability of an additional event;
• and
• identifying the additional event based on the third probability of the additional event and the one or more operations.