Control device of vehicle drive device, vehicle-mounted electronic control unit, trained model, machine learning system, method of controlling vehicle drive device, method of producing electronic control unit, and output parameter calculation device

A vehicle-mounted electronic control unit comprising

• a parameter acquiring part configured to acquire at least one first input parameter of a vehicle at a first point of time, at least one second input parameter of the vehicle at a second point of time after the first point of time, and at least one output parameter of the vehicle at a point of time after the second point of time, and send the at least one first input parameter, the at least one second input parameter, and at least one output parameter through a communication device provided at the vehicle to a server at an outside of the vehicle, and
• a processing part configured to receive a trained model using a neural network from the server through the communication device and using the trained model to calculate the at least one output parameter, wherein
• the neural network includes a first input layer to which the at least one first input parameter is input, a second input layer to which the at least one second input parameter is input, a first hidden layer to which outputs of the first input layer are input, a second hidden layer to which at least one value correlated with the outputs of the first hidden layer, and outputs of the second input layer are input, and an output layer outputting the at least one output parameter, and
• the server uses the at least one first input parameter, the at least second input parameter, and the at least output parameters acquired by the parameter acquiring part as sets of training data to generate the trained model.

A machine learning system comprising an electronic control unit provided at a vehicle, a communication device provided at the vehicle, and a server at an outside of the vehicle, wherein,

• the electronic control unit comprises
• a parameter acquiring part configured to acquire at least one first input parameter of the vehicle at a first point of time, at least one second input parameter of the vehicle at a second point of time after the first point of time, and at least one output parameter of the vehicle at a point of time after the second point of time, and send the at least one first input parameter, the at least one second input parameter, and the at least one output parameter through the communication device to the server, and
• a processing part configured to receive a trained model using a neural network from the server through the communication device and use the trained model to calculate the at least one output parameter, wherein
• the neural network includes a first input layer to which the at least one first input parameters is input, a second input layer to which the at least one second input parameter is input, a first hidden layer to which outputs of the first input layer are input, a second hidden layer to which at least one value correlated with the outputs of the first hidden layer, and outputs of the second input layer are input, and an output layer outputting at least one output parameter, and
• the server is configured to use the at least one first input parameter, the at least one second input parameter, and the at least one output parameter acquired by the parameter acquiring part as sets of training data to generate the trained model.

A method of controlling a vehicle drive device comprising

• acquiring at least one first input parameter of a vehicle at a first point of time and at least one second input parameter of the vehicle at a second point of time after the first point of time,
• using a trained model using a neural network to calculate at least one output parameter of the vehicle based the at least one first input parameter and the at least one the second input parameter, and
• controlling a vehicle drive device mounted in the vehicle based on the at least one output parameter, wherein
• the neural network include a first input layer to which the at least one first input parameter is input, a second input layer to which the at least one second input parameter is input, a first hidden layer to which outputs of the first input layer are input, a second hidden layer to which at least one value correlated with the outputs of the first hidden layer, and outputs of the second input layer are input, and an output layer outputting the at least one output parameter.