**1. Introduction**

The evaluation of the quality of goods is based on human feelings in many areas, such as works of art [1,2], consumer electronics [3,4], cars [5,6], and other products [7,8]. Subjective evaluations are as important as objective metrics for engineering products because consumers evaluate the value of products based on their feelings. However, subjective evaluations are easily affected by the experience level of the evaluator, geographical locations, and the time of evaluation. Furthermore, subjective evaluations generally take a longer time than an objective evaluation. Therefore, subjective evaluation has a lower consistency and lower repeatability than measurement-based objective evaluations. Subjective evaluations also require a finished product, so feedback from a subjective evaluation cannot be considered in the beginning phase of the product design process.

Much effort has been made to take into account subjective evaluations in earlier design phases. A mockup can be used if the subjective evaluations are based on visual and tactile aspects, such as the hands-on feel of handheld devices [9]. If the evaluations are based on a comprehensive feeling, a designer can use a correlation model between the objective and subjective evaluations with a mathematical product model that generates virtual measurements for an objective evaluation, such as vehicle ride comfort [10,11], vehicle steering feel [12,13], and art education [14].

In the automotive industry, objective evaluations of ride comfort and steering feel are inevitable and crucial during the development process. Furthermore, they cannot be substituted with any other objective evaluation metrics because customers judge cars through their feel. Subjective evaluations of a car can only be performed in the very last phase of the development process, so engineers have very little chance to modify the design parameters when the subjective evaluations become available.

Professional evaluators or special test drivers are required for subjective evaluations, which can be performed only several times per day because modification of the testing car or the testing environment takes time. Due to these constraints, several approaches have been suggested to predict the results of a subjective evaluation using values measured from a real car or a simulation model. Some of examples are linear regression. Data et al. [15] investigated a method to find the best correlation between objective metrics and subjective ratings. Rothhämel et al. [16] proposed a method to find correlations using a driving simulator and a vehicle model. Nybacka and coworkers [17,18] identified the links between objective metrics using a steering robot and the subjective evaluation of expert drivers. Gil Gómez and coworkers [19,20] found correlations between objective metrics. The other methods that have been used are nonlinear regression including a fuzzy model [21] and an artificial neural network [17–20,22]. In References [17–20], a simple neural network with two hidden layers is used. Liu et al. [22] investigated a method to find correlations between signals measured by an electromyogram and subjective evaluations. However, the methods have shown a low correlation and are e ffective for only limited cases because a designer selects the correlation variables between the objective measurements and the subjective evaluation, so the su fficiency and optimality of the selection are not clear. Furthermore, the generality of such correlation models is not guaranteed due to the small number of evaluation data.

Deep neural network (DNN) techniques are actively applied to the design of correlation models between objective measurements and subjective evaluations. For subjective video quality evaluations, a subjective video quality prediction model was introduced based on a DNN. Varga [23] evaluated video quality through a DNN architecture consisting of a pre-trained network, transfer learning, temporal pooling, and regression layers. In the medical field, DNN techniques are also widely used. Mahendran et al. predicted major depressive disorder using a weighted average ensemble machine learning model [24]. Weber et al. [25] calculated muscle fat infiltration using a previously developed convolution neural network. In the material field, Yao et al. used the neural network model to predict subjective tactile properties from objective test results of porous polymeric materials [26].

In the automotive field, an interesting result has been reported on regarding modeling the correlation between objective and subjective evaluations of vehicle dynamic performance using a DNN technique [27]. A method was presented to identify the relationship between the objective metrics and subjective assessments. A quite meaningful correlation model was generated and can foresee the subjective characteristics of a new vehicle based on a simulation and measurements. The correlation model was trained using 22 test drivers with 51 vehicles. The number of training sets did not seem to be su fficient compared to general DNN cases. However, the number was quite large when considering the number of test drivers and cars used for a general vehicle evaluation. For typical subjective evaluations, a few test drivers drive a few cars (a test car and few reference cars), and the process takes several days. A lack of su fficient datasets is a major di fficulty in applying DNN techniques for the objectification of the subjective evaluation of cars. Even though the results were quite impressive and successful, the robustness and generality of the model are not clear due to the small training datasets.

Another weakness of this approach is that the inputs to the model are predefined objective metrics, such as the yaw gain, torque dead band, and phase time lag. The use of predefined metrics raises questions about the appropriate definitions of the metrics, the selection of the best metrics, and whether the selected metrics represent human perceptions well.

Not all artificial intelligent techniques require a large datasets. Mordvintsev et al. [28] modified an input image such that the output from a given pre-trained neural network would be as close to the expected output as possible. This method is called Deep Dream and has been used to synthesize two images to create a new image. An image synthesis method called artistic style transfer was also developed [29], which synthesizes two images by transforming the style of one image to the other image. This technique needs only two images: one for the style and the other for the content.

In this method, the network parameters are not optimized, and transforming the style of the input image requires a recursive computational or training process. The network structure and parameters use those of a pre-trained network, such as VGG-19 [30]. VGG-19 is a pre-trained convolution neural network that was developed by the Visual Geometry Group of the University of Oxford for classification tasks. This method numerically extracts the style of an image that is recognized by human senses from a raw image without using predefined metrics, such as lines or edges.

This paper presents a method to build a correlation model between measurements and subjective evaluations without using predefined features or objective metrics, as shown in Figure 1. The key idea is that a numerical representation of ride comfort is extracted from raw signals that were measured in a test vehicle without preprocessing to define and calculate objective metrics. This method is based on the ideas of the artistic style transfer method. The proposed method was applied to the evaluation of ride comfort when a vehicle passes over a speed bump. A comparative model is proposed for the ride comfort of two vehicles to minimize the e ffect of using a small dataset. The input of the model is the measurements from the two vehicles, and the output is the di fferences in their subjective ratings of ride comfort.

**Figure 1.** Objectification of the subjective evaluation of ride comfort.

The rest of the paper is organized as follows. Section 2 presents the method for the objectification of subjective evaluation, and Section 3 presents the results of the model training. Section 4 suggests possible applications of the model, and Section 5 concludes the paper.
