## 4 Micro-Driving Behavior
### Micro-Behavior Comparison of Four Combined Curves  
Previous studies have shown that it is necessary to study the different types of combined curves separately [17]. In addition, the micro-driving behavior may vary according to the type of combined curve. To determine which behavior requires more attention in the development of AVs and ADASs, it is advisable to compare the variation of speed changes and lane departure behavior changes for each type of curve. The Shapiro-Wilk and Kolmogorov-Smirnov methods were applied to conduct normality tests on the original data. They found that the speed change data and the lane departure data rejected the assumption of a normal distribution for data at a 0.05 significance level. Taking into account the non-normal distribution of the data, the article uses the coefficient of variation to measure the variability of the data instead of using the variance. The coefficient of variation is a statistical measure used to measure the relative variability of data. The formula is as follows:  
\[CV=\left|\frac{\textit{Standard~{}Deviation}}{\textit{Mean}}\right|\times 100 \tag{2}\]  
If the coefficient of variation of the speed change exceeds that of the lane departure, it indicates that the vehicle's speed is changing frequently during operation, and the magnitude and frequency of these speed changes are greater than the lane departure changes. AVs and ADASs should pay more attention to speed changes during the curve. If the coefficient of variation of lane departure is larger, it suggests that the driver has inadequate lateral stability and is more susceptible to lane departure while driving. Priority should be given to lane departure with AVs and ADASs. Due to the differing units of measurement with speed change in km/h and lane departure in m, the Z-score standardized method was used to standardize the differences in speed and lane departure. Considering that the calculation of the coefficient of variation includes the mean and that the Z-score standardized method gives a mean of zero and that the data are not normally distributed, the use of the median as a substitute for the mean provides a better description of the basic characteristics of the data. Therefore, the method of calculating the coefficient of variation has been optimized using the median instead of the mean. \[CV=\left|\frac{\textit{Absolute~{}Median~{}Deviation}}{\textit{Median}} \right|\times 100 \tag{3}\]  
The vehicle operation data of 30 drivers were used for each combined curve. The results are shown in Table 5. \begin{table}
\begin{tabular}{c c c c c} \hline
**Type** & **Mean (m)** & **Max (m)** & **Min (m)** & **S.D (m)** \\ \hline IDCF lane & Maximum lateral departure & 0.81 & 1.10 & 0.01 & 0.29 \\ departure & Departure persistence distance & 72.75 & 500 & 5 & 70.48 \\ ADCF lane & Maximum lateral departure & 0.36 & 1.52 & 0.00 & 0.35 \\ departure & Departure persistence distance & 60.64 & 410 & 5 & 61.93 \\ \hline \end{tabular}
\end{table} 
Table 4: Statistical description of the two types of lane departure. Table 5 shows the results of the coefficient of variation of the micro-driving behavior on four combined curve types. The speed change behavior requires more attention than the lane departure behavior for the sag curve, as the former experiences a higher coefficient of variation. Conversely, for upslope curves and crest curves more attention should be paid to the lane departure behavior, where the lane departure variation exceeds that of the speed change. On the downslope curve, both speed change and lane departure have the highest coefficient of variation. Referring to previous research, significant speed increases are more likely to occur on downslope curves [17], so speed change requires more attention. Table 5 shows that the downslope curve has the highest coefficient of variation of speed change (15.884) of the four combined curve types, indicating the worst speed change. In contrast, the upslope curve has the highest consistency of vehicle speed (4.436).