**5. Conclusions**

We have proposed a methodology to objectify subjective assessments using a pre-trained DNN. The proposed method does not require any feature definition or objective metric definition to extract ride comfort from measured signals. A DNN technique called artistic style transfer was used to extract a numerical form of the driving comfort without any predefined features, and then a comparative model was designed. The model showed a higher accuracy than any other correlation models in the literature. This was because of use of CNN in extracting performance metrics and use of a comparative model. The limitations of this research include a small number of test drivers, few test surfaces, and the limited number of datasets. These limitations can be a huge barrier to the use of a DNN, and thus, a general evaluator model could not be achieved with the given dataset. The limitations are typical and unavoidable for the subjective evaluation of vehicle ride comfort due to the expensive and long evaluation process. Therefore, the designed evaluator model does not work for all kinds of general conditions. Rather, the designed model can be used as a numerical evaluator model for the given road surface with the given vehicle speed. During the research, the authors concluded that designing a general evaluator model for vehicle subjective evaluation working for any conditions is practically impossible and designing several case-dependent models for each different test condition would be practically viable. The authors showed that the proposed method was effective for such cases. Even though the proposed method itself can be applied to both general and case-dependent models, the strength of the proposed method lies on the design of a subjective evaluation model when there is a small number of data points.

**Author Contributions:** Writing—original draft preparation, D.K.; data collection and formal analysis, M.J.; funding acquisition and project administration, B.B.; supervision, writing—review and editing, C.A.

**Funding:** This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (No. NRF-2019R1A2C1003103).

**Conflicts of Interest:** The authors declare no conflict of interest.
