Abstract: The present invention aims to improve the efficiency of automobile development by front loading the evaluation of an on-road driving test, and comprises a virtual evaluation step of simulating the on-road driving test using a driving environment model which models a driving environment, an operating state model which models an operating state, and a vehicle model which models the actual vehicle; a bench test evaluation step of imitating the on-road driving test by combining a part or all of the actual vehicle, a part or all of the vehicle model, a part or all of the driving environment model, and a part or all of the operating state model, and conducting a bench test of a part or all of the actual vehicle, and an on-road driving test evaluation step of conducting the on-road driving test of the actual vehicle in a real driving environment.

Abstract: The present invention is a verification-cycle generation method for generating a verification cycle for vehicle development, the verification cycle being a realistic one and satisfying boundary conditions for a plurality of vehicles, wherein a plurality of datasets concerning a drive cycle are input, the plurality of datasets are individually clustered and classified into a plurality of data groups, a plurality of verification cycles concerning vehicle development are generated on the basis of the plurality of clustered data groups, each of the plurality of verification cycles is carried out through simulation or with an actual machine, and a representative cycle that best satisfies user-defined criteria is selected from among the plurality of verification cycles.

Abstract: The present invention is to accurately obtain a vehicle element response data under a desired driving environment by simulation without performing actual road driving and is a vehicle element response learning method for generating a trained model related to a response of a vehicle element that is a vehicle or a part of the vehicle, and the method includes: (1) an input step of giving an input including parameters related to a vehicle speed, a load, and a temperature assuming actual road driving, to the vehicle element; (2) an acquisition step of acquiring response data of the vehicle element and acquiring, as training data, input data representing the input and the response data; and (3) a generation step of generating the trained model related to the response of the vehicle element, from the training data by using machine learning.

Abstract: The present invention aims to improve the efficiency of automobile development by front loading the evaluation of an on-road driving test, and comprises a virtual evaluation step of simulating the on-road driving test using a driving environment model which models a driving environment, an operating state model which models an operating state, and a vehicle model which models the actual vehicle; a bench test evaluation step of imitating the on-road driving test by combining a part or all of the actual vehicle, a part or all of the vehicle model, a part or all of the driving environment model, and a part or all of the operating state model, and conducting a bench test of a part or all of the actual vehicle, and an on-road driving test evaluation step of conducting the on-road driving test of the actual vehicle in a real driving environment.