# Path Planning Strategy for Vehicle Navigation Based on User Habits
## 3 Results  
### Emulation  
To verify the proposed theory and the effectiveness of the algorithm, we use a road network environment with a 5 \(\times\) 5 grid simulated according to ideal settings from top to bottom and from left to right in the order of the road traffic environment simulation. The grid performance simulation of traffic assignment, the network structure, and the performance simulation of road vector are shown in Figure 7 and Table 4.  
We verify the reliability of the LFCM algorithm in driving style clustering under different conditions. Without loss of generality, the time-based, time-economic-based, and time-economic-comfort-based users are randomly selected as examples from category \(c_{3}^{1},c_{3}^{2},c_{3}^{3}\), respectively (Table 4). The clustering results are shown in Table 5, where \"\(\surd\)\" and \"\(\_\)\" represent valid and invalid features, respectively. For ease of description, we present the type of time-based driving style as an example. Step 1: Suppose a certain time-based driving style user A. Following his habits and the road performance vector shown in Table 4, 20 groups of different starting points and destination path combinations are selected as log samples. Step 2: The smallest road vector elements containing users' driving information from the 20 log samples are obtained by the sampling operation. The fuzzy C-means algorithm is used on the tuple cluster of the log sample vector. Step 3: The validity of the eigenvectors of the 20 groups of clustering results is determined. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Road Number**} & \multicolumn{2}{c}{**Standard Road Performance (\(\mathbf{U_{\
u}}\), \(\mathbf{A_{\
u}}\))**} \\ \hline \multirow{3}{*}{1-12} & [0.77,0.50,0.45](10,34,74,0.38) & [0.56,0.69,0.72](10,49,0.59,0.21) \\  & [0.73,0.69,0.66](10,66,0.60,0.78) & [0.47,0.41,0.79](10,21,0.37,0.67) \\  & [0.69,0.40,0.46](10,47,0.52,0.50) & [0.57,0.64,0.33](10,68,0.39,0.59) \\ \hline \multirow{3}{*}{13-24} & [0.75,0.70,0.39](10,64,54,0.78) & [0.31,0.42,0.64](10,44,0.62,0.45) \\  & [0.76,0.53,0.65](10,75,0.47,0.36) & [0.45,0.62,0.64](10,74,0.57,0.76) \\  & [0.23,0.68,0.61](10,47,0.73,0.69](10,51,0.70) & [0.21,0.21 Step 4: The 20 log samples are compared and counted to obtain the driving style and the presumed driving style. The coincidence rate of the driving style clustering results with actual statistics is above 90% (Table 5), which can meet practical requirements. Based on the clustering results, the performance of each road can be quantified in a personalized way according to the evaluation method in Table 3. Without loss of generality, the latest 20 sample log trajectories of a random time-economic-based user are selected as an example of personalized user quantification. Step 1: The 20 groups of clustering data acquired from the user log samples are screened to eliminate false clustering data. Step 2: The corresponding terms of the filtered clustering data are weighted and averaged to obtain the statistical driving feature vector: \(W\) = [0.56,0.37,0.07]. Step 3: In accordance with Formula (19), the quantitative results are shown in Table 6.  
\[\omega=\omega_{\text{T}}\lambda_{\text{T}}+\omega_{\text{M}}\lambda_{\text{M}} \tag{19}\]  
According to the personalized path performance shown in Table 6, we reassign the road network performance and use the ant colony algorithm to achieve personalized dynamic route selection based on user driving habits, as shown in Figure 8, in which the blue route is a random track in the user log samples, the green line segment is the personalized path of the proposed algorithm, and the red part is the coincidence of the two paths. Figure 8 shows that the log sample trajectory from the starting point to the end point is 6-17-23-29-40-46-47-53-59-60 while the integrated user habits in the personalized planning path are presented as 6-17-23-29-40-46-52-58-59-60. The number of anastomosed sections is 9, the number of different sections is 2, and the absolute anastomosis rate is 82%.