# Path Planning Strategy for Vehicle Navigation Based on User Habits
## 4 Conclusions  
We propose a personalized path decision algorithm that is based on user habits, implementing the navigation service from a previous similar trip group to a specific individual, which can essentially improve the personalization of the existing path planning algorithms. the results and shortcomings of the existing optimization algorithms based on a single optimization target or a single or a few special traffic factors are analyzed. We use the log road track that integrates user's habits to mine driving characteristics, and use LFCM to achieve user's driving style clustering, and get users' driving habits. Then, we use T-S based road multi index performance synchronous evaluation model to quantify the road's time, economy and comfort performance. At the same time, we combine user's driving style and feature to get the user's road performance. Finally, ant colony algorithm is used to search the shortest path consumption path. The algorithm is based on the user's own log trajectory and uses feature mining and clustering techniques to get the user's habits and implements the navigated service objects from the previous travel groups to the travel individual and customizes personalized path navigation system for users to meet their own driving habits. The experimental results show that the absolute anastomosis rate and the relative anastomosis rate of the method in the simulated traffic network are 82% and 100%, respectively. In the actual traffic environment, three comparative examples (i.e., A, B, and C) of user's personalized path and the corresponding user log path show consistent rates, reaching 89.2%, 100%, and 84.5%, respectively; in comparison with the individual path in the literature [20], the path coincidence rate significantly increases by 36.9%, 35.3%, and 35.8%, respectively. By improving the personalization level of existing path planning, the travel experience of self-driving users is greatly improved.  
**Acknowledgments: This project is partially supported by the National Natural Science Foundation of China (Grant nos. 61663011), the Provincial Natural Science Foundation of Jiangxi (Grant nos. 20161 BAB212053), the postdoctoral fund of Jiangxi Province(2015 KY19), and the National Natural Science Foundation of China (Grant nos. 51609088).**  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Customer Type** & **Path Type** & \(l_{\text{same}}/\text{km}\) & \(l_{\text{total}}/\text{km}\) & **A/a.u.** \\ \hline \multirow{2}{*}{A} & Bibliographic reference path & 15.69 & 30.01 & 52.3\% \\  & Personalization path based on A/s habits & 26.77 & 30.01 & 89.2\% \\ \hline \multirow{2}{*}{B} & Bibliographic reference path & 18.71 & 28.94 & 64.7\% \\  & Personalization path based on B’s habits & 28.94 & 28.94 & 100\% \\ \hline \multirow{2}{*}{C} & Bibliographic reference path & 14.59 & 29.96 & 48.7\% \\  & Personalization path based on C’s habits & 25.33 & 29.96 & 84.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 15: Verification of path anastomosis.  
**Author Contributions:** [PERSON] contributed to the conception of the reported research. [PERSON] contributed significantly to the design and conduct of the experiments and the analysis of results, as well as contributed to the writing of the manuscript. [PERSON] helped design the experiments and perform the analysis with constructive discussions. [PERSON] helped deal with data and plot the graphs.  
**Conflicts of Interest:** The authors declare no conflict of interest.