# Path Planning Strategy for Vehicle Navigation Based on User Habits
## 3 Results
### Real Routine Verification
#### 3.2.2 Validation of the Effectiveness of Personalized Planning  
Based on the basic evaluation model for the reliable evaluation of the results of the synchronization performance index, three drives (A, B and C) in two categories, who own their own log trajectory records, are randomly selected from the seven kinds of small classifications of driving users (Table 3) to the performance of the path planning algorithm. Specifically, inter-class experiments based on different type users A and B are designed to verify the universality of the algorithm. Meanwhile, intra-class validation experiment based on different type users B and C, who are in same type but possess different driving feature vectors are added to further verify the personalization level of algorithm. The validation verification framework is design as in Table 10. To facilitate the comparison of path planning results, this paper is based on the background of route guidance in Changsha City which is same as that of Reference [25], as well as the user types. \(\bullet\)Inter-class validation  
(1) Example 1  
The class design verification in Table 10 shows that the economy + comfort user A ([0.05,0.32,0.63]) travels with only economy and comfort in mind and thus ignores the time factor. For this user, the economic attention level is 2, the influence coefficient is small at 0.32, the level of the concern for comfort is 1, and the influence coefficient is up to 0.63. The individual values of road performance depend only on the economy and comfort factors. Based on the driving characteristics of user A, the case is weight coupled with the time, economy, and comfort performance values obtained by the evaluation model. Moreover, the individual road performance values in field A are obtained. Then, the best path is searched according to the road's personalized cost. In view of the large complexity of road networks, this example only enumerates the personalized performance and effective length of the road related to the path search results, as shown in Table 11, where the effective length of the road is the length of the path through the planning path. In Table 11, the personalized performance of each road in the personalized path that integrates user A's habits is higher than the personalized performance of the roads in the reference path, which lays the foundation for the integration of personalized path performance advantages of user A driving habits. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Validation Category** & **Driver** & **Style** & **Feature Vector** \\ \hline \multirow{2}{*}{Inter class validation} & A & Economy-comfort & [0.05,0.32,0.63] \\  & B & Time-economic & [0.35,0.57,0.08] \\ \hline \multirow{2}{*}{Intra class validation} & B & Time-economic & [0.35,0.57,0.08] \\  & C & & [0.56,0.37,0.07] \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Example validation framework. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Road Name** & **Level** & **T** & **M** & **C** & **Error Vector** & \(\sigma\) \\ \hline third ring road & 1 & 0.75 & 0.15 & 0.68 & [\(-\)0.05,0.03\(-\)0.09] & 0.062 \\ Wan Jiali yauduct & 2 & 0.67 & 0.65 & 0.59 & [\(-\)0.03,\(-\)0.07,0.01] & 0.044 \\ Shaoshan South Road & 3 & 0.42 & 0.25 & 0.43 & [\(-\)0.03,\(-\)0.01,\(-\)0.05] & 0.034 \\ West Second Ring & 3 & 0.46 & 0.31 & 0.45 & [0.01,0.05\(-\)0.03] & 0.034 \\ ThreeFenghion Road & 4 & 0.53 & 0.33 & 0.54 & [0.03,\(-\)0.02,0.02] & 0.024 \\ The middle of the people Road & 5 & 0.30 & 0.19 & 0.41 & [\(-\)0.05,\(-\)0.01,0.01] & 0.030 \\ Binhu West Road & 6 & 0.43 & 0.32 & 0.40 & [0.03,\(-\)0.02,\(-\)0.04] & 0.031 \\ The road of literature and art & 7 & 0.34 & 0.27 & 0.32 & [0.04,\(-\)0.02,0.03] & 0.031 \\ Binjiang landscape road & 8 & 0.42 & 0.38 & 0.60 & [0.02,0.01,\(-\)0.01] & 0.014 \\ Xiangjiang Middle Road & 8 & 0.48 & 0.33 & 0.63 & [0.08,\(-\)0.04,0.02] & 0.053 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The time, economy, and comfort performance of iconic road.