System and method for providing road user classification training using a vehicle communications network

A system for providing road user classification training using a vehicle communications network, comprising:

• a memory storing instructions when executed by a processor cause the processor to:
• analyze sensor data to determine a sensor classification of at least one road user, wherein the sensor data is provided by the vehicle and analyzed by a neural network to determine the sensor classification;
• analyze at least one message that is received through the vehicle communications network to determine a vehicle-to-everything (V2X) classification of the at least one road user, wherein the at least one message is associated with the at least one road user;
• determine if a variance exists between the V2X classification of the at least one road user and the sensor classification of the at least road user, wherein data points included within the V2X classification of the at least one road user are compared against data points included within the sensor classification of the at least one road user provided by the neural network to determine if a delta exists between the data points included within the V2X classification and the data points included within the sensor classification;
• and
• train the neural network with classification variance data upon determining that the variance exists between the V2X classification and sensor classification based on the delta between the data points included within the V2X classification and the data points included within the sensor classification, whereintraining the neural network with the classification variance data supplements the variance that exists between the V2X classification and the sensor classification of the at least one road user.

A non-transitory computer readable storage medium storing instructions that when executed by a computer, which includes a processor, perform a method, the method comprising:

• analyzing sensor data to determine a sensor classification of at least one roaduser, wherein the sensor data is provided by a vehicle and analyzed by a neural network to determine the sensor classification;
• analyzing at least one message that is received through a vehiclecommunications network to determine a vehicle-to-everything (V2X) classification of the at least one road user, wherein the at least one message is associated with the at least one road user;
• determining if a variance exists between the V2X classification of the at least one road user and the sensor classification of the at least road user, wherein data points included within the V2X classification of the at least one road user are compared against data points included within the sensor classification of the at least one road user provided by the neural network to determine if a delta exists between the data points included within the V2X classification and the data points included within the sensor classification;
• and
• training the neural network with classification variance data upon determining that the variance exists between the V2X classification and sensor classification based on the delta between the data points included within the V2X classification and the data points included within the sensor classification, wherein training the neural network with the classification variance data supplements the variance that exists between the V2X classification and the sensor classification of the at least one road user.