## 5 Discussion
### Methods Validation  
In this paper, an entropy-weight-based VIKOR method is proposed to optimize the scheme of in-vehicle interface design. A case study involving an HUD concept design was employed to demonstrate the proposed method. To verify the validity of the entropy weight and VIKOR design scheme evaluation and optimization method, we compared the proposed method with other decision-making methods, e.g., the FCE (fuzzy comprehensive evaluation) [45], the GRA (grey relational analysis) [46] and the TOPSIS method [47]. In addition, the schemes' evaluation values and ranking results obtained with the three methods are shown in Table 5.  
It can be seen from Table 5 that scheme 1 is the first-rate all along. Except for the deviation in ranking between scheme 2 and scheme 3 of the FCE method, the ranking of the other schemes is consistent, which proves the feasibility and rationality of the proposed method. Next, we will discuss the advantages of this proposed method and the other three MCDM methods respectively.  
Compared to the fuzzy comprehensive evaluation method, the evaluation of the relative importance of each indicator element in the fuzzy comprehensive evaluation method had a degree of subjective uncertainty and could not solve the problem of repeated evaluation information caused by the correlation between indicator factors. In this paper, the entropy weight method was used to reduce the subjective factors in the weighting and a more objective weight model for design scheme evaluation was established to make the evaluation results more accurate. In addition, we found very few differences between the values of each scheme in the fuzzy comprehensive evaluation method and there will be ranking deviation (Table 5), and the order of scheme 2 and scheme 3 is opposite.  
Compared to the TOPSIS method, the difference between the evaluation values calculated by the TOPSIS method was quite minor and the distribution was dense. However, the excessively accurate ranking appeared in reverse order and the final result was not necessarily the optimal scheme. In the ranking process using the VIKOR method, the advantages of the evaluation schemes were ranked by the comparison of group utility value, regret value and comprehensive utility value. The optimal scheme obtained using this method was the closest to the ideal scheme. As shown in Table 5, the difference between the scheme values calculated by the TOPSIS method was small, which may not be suitable for decision-making in mass schemes, and the VIKOR method will show greater advantages on this occasion.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Scheme 1** & **Scheme 2** & **Scheme 3** & **Ranking** \\ \hline Proposed method & 0.00 & 0.63 & 1.00 & \(1>2>3\) \\ FCE & 0.78 & 0.13 & 0.22 & \(1>3>2\) \\ GRA & 0.81 & 0.47 & 0.39 & \(1>2>3\) \\ TOPSIS & 0.15 & 0.39 & 0.48 & \(1>2>3\) \\ \hline \hline \end{tabular} Note: The FCE is expressed by comprehensive evaluation value; the larger the evaluation value, the better the scheme. The TOPSIS method is expressed by the degree of closeness to the positive ideal scheme; the smaller the evaluation value, the better the scheme. The GRA method is expressed by grey correlation degree; the larger the evaluation value, the better the scheme.  
\end{table}
Table 5: Evaluation results using different methods.  
When it comes to the grey relational analysis method, we generally invited several industry experts to score the schemes and obtain the grey correlation value, which was highly subjective and the difference between the decision-making values of each scheme was small. As shown in Table 5, the difference between the maximum value and the minimum value was only 0.42. When there are many schemes, however, it is not conducive for the decision-makers to make more objective judgments.