## 6 Conclusions  
Aiming at the quantitative evaluation of driving style and revealing transition properties between driving style and behavior, a novel evaluation method based on the State-Action semantic plane was proposed. Through comparison with subjective and conventional approaches, the validity and reliability of this approach were verified, and the classification accuracy of the driving style was thus improved. Conclusions are summarized as follows. 1. The HDP-HSMM algorithm combines the advantages of infinite clustering and adaptive updating in the HDP algorithm with the description of the dynamic random process in HSMM. It can decompose time series driving data into the fragment clusters with similar characteristics. This algorithm can be further used for the characteristic extraction of a large amount of naturalistic driving data. 2. The driving behavior semantic plane was developed. It can interpret the fragment clusters, quantify the drivers' risk indices by determining the probability and duration proportion of each behavior, and intuitively express the driving preferences of drivers with different styles. The aggressive drivers prefer NFNA and NFNA, high-risk driving behaviors, in which probability frequency reaches 79.7% and the duration proportion reaches 75.4%. The cautious drivers prefer low-risk driving behaviors, such as FFNA and FFND, with a probability of 66.67% and a duration proportion of 61.04%. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Correlation** & \begin{tabular}{c} **MFND–** \\ **MFNA** \\ \end{tabular} & \begin{tabular}{c} **NFAD–** \\ **NFND** \\ \end{tabular} & \begin{tabular}{c} **NFND–** \\ **NFNA** \\ \end{tabular} & \begin{tabular}{c} **FFND–** \\ **FFNA** \\ \end{tabular} & \begin{tabular}{c} **FFNA–** \\ **FFND** \\ \end{tabular} & \begin{tabular}{c} **MFNA–** \\ **NFND** \\ \end{tabular} \\ \hline \begin{tabular}{c} **MFND–** \\ **MFNA** \\ \end{tabular} & \(1\) & \(0.1494\) & \(0.0687\) & \(0.0679\) & \(-0.2143\) & \(-0.1598\) & \(0.0857\) \\ \hline \begin{tabular}{c} **NFAD–** \\ **NFND** \\ \end{tabular} & \(0.1494\) & \(1\) & \(\mathbf{0.5621}\) & \(\mathbf{0.6491}\) & \(\mathbf{-0.4193}\) & \(\mathbf{-0.4520}\) & \(\mathbf{0.4578}\) \\ \hline \begin{tabular}{c} **NFAD–** \\ **NFNA** \\ \end{tabular} & \(0.0687\) & \(\mathbf{0.5621}\) & \(1\) & \(0.2014\) & \(-0.2554\) & \(-0.1145\) & \(\mathbf{0.6391}\) \\ \hline \begin{tabular}{c} **NFND–** \\ **NFAD** \\ \end{tabular} & \(0.0679\) & \(\mathbf{0.6491}\) & \(0.2014\) & \(1\) & \(-0.2336\) & \(-0.3098\) & \(0.3264\) \\ \hline \begin{tabular}{c} **FFND–** \\ **FFNA** \\ \end{tabular} & \(-0.2143\) & \(\mathbf{-0.4193}\) & \(-0.2554\) & \(-0.2336\) & \(1\) & \(0.5029\) & \(-0.1657\) \\ \hline \
\begin{tabular}{c} **FFNA–** \\ **NFND** \\ \end{tabular} & \(-0.1598\) & \(\mathbf{-0.4520}\) & \(-0.1145\) & \(-0.3098\) & \(0.5029\) & \(1\) & \(-0.1336\) \\ \hline
\begin{tabular}{c} **MFNA–** \\ **NFND** \\ \end{tabular} & \(0.0857\) & \(\mathbf{0.4578}\) & \(\mathbf{0.6391}\) & \(0.3264\) & \(-0.1657\) & \(-0.1336\) & \(1\) \\ \hline \hline \end{tabular} Note: Significant at the \(0.01\) level.  
\end{table} 
Table 6: Pearson correlation coefficients between the seven transition probabilities.