## 4 The Performance of the Optimized SVR Model on Driving Decision-Making
### Sensitive Analysis of Road Conditions on Driving Decisions  
It can be seen from the above results that road conditions have a great influence on driving decisions. But how does each parameter affect driving decisions? What is the degree of their effects on each driving decision? A solution is provided to quantitatively evaluate their effects with the SVR-DDM with weighted hybrid kernel function (all of the DDMs mentioned in the following analysis refer to the SVR-DDM with weighted hybrid kernel function and with added road conditions). We quantitatively evaluate the effects of each road parameter on driving decisions by analyzing the sensitivity of DDM to the changes in each road parameter. We take the changes in the road adhesion coefficient \(\mu\) as an example. Using the driving decision samples under standard road conditions, we first count and calculate the proportions of each driving decision at different traffic density ranges. Then, we make the \(\mu\) take values at 0.55, 0.28 and 0.18, respectively. The other three road parameters remain standard. Every time that the \(\mu\) changes, a new set of driving decision samples is obtained and input into the DDM. From the output of DDM, the proportion of each driving decision in different traffic density is calculated. Then, we can get the trend that the proportion of each driving decision varies with the traffic density when \(\mu\) taken at 0.75, 0.5, 0.25 and 0.18, respectively. In the same way, we can also get the trend that the proportion of each driving decision varies with the traffic density when the other three road parameters take different values, respectively. After this operation and data statistics, the quantitative influence is displayed in Figure 9. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Driving Decision** & **Reasoning Accuracy Rate** & \(E_{\mathbf{M}}\) & \(E_{\mathbf{R}}\) &
\begin{tabular}{c} **Average Reasoning Time** \\ \end{tabular} \\ \hline SVR-DDM with & Free driving & 82.3\% & 0.145 & 1.566 & \\ weighted hybrid & Car following & 85.9\% & 0.138 & 1.441 & 0.003 s \\ kernel function & Lane changing & 78.2\% & 0.158 & 1.845 & \\ \hline SVR-DDM with RBF & Free driving & 78.5\% & 0.157 & 1.634 & \\ kernel function & Car following & 82.2\% & 0.155 & 1.521 & 0.002 s \\  & Lane changing & 76.8\% & 0.173 & 1.848 & \\ \hline \multirow{3}{*}{BPNN-DDM} & Free driving & 78.1\% & 0.171 & 1.859 & \\  & Car following & 80.4\% & 0.159 & 1.723 & 0.006 s \\ \cline{1-1}  & Lane changing & 75.1\% & 0.176 & 1.877 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The results of three DDMs without considering road conditions. Figure 9: Driving Decision Rate under Different Road Conditions. (The horizontal axis in these diagrams represents the traffic flow density, and the vertical axis represents the rate of each driving decision (between 0 and 1). The solid lines in all diagrams represent the changing trend of the proportion of driving decisions with traffic density under standard road conditions. (**a**) The changing trend of each driving decision rate with traffic density when \(\mu\) takes different values; (**b**) The changing trend of each driving decision rate with traffic density when \(\rho\) takes different values; (**c**) The changing trend of each driving decision rate with traffic density when \(\tau\) takes different values; (**d**) The changing trend of each driving decision rate with traffic density when \(\delta\) takes different values. From left to right, each column represents the trend of lane changing rate, free driving rate and car-following rate with traffic density when each road parameter is taken as different values, respectively.). As shown in Figure 9, it can be seen that the changes of road conditions have the greatest influence on the driving decisions in the low traffic density range (4-16 veh/km) and almost have no influence in the high traffic density range (28-32 veh/km).