# Path Planning Strategy for Vehicle Navigation Based on User Habits
## 2 Materials and Methods
### Mining and Clustering of Users' Driving Habits
#### 2.1.2 Acquisition of Individualized Driving Styles  
Based on the fuzzy C-means algorithm [28], we integrate driving behavior characteristics and introduce the log fuzzy C-means algorithm (LFCM) to guide the clustering of driving characteristics in the style space. The LFCM algorithm is shown in Figure 1.  
* Selection of style clustering center
* Mining of driving characteristics  
Driving characteristics in log trajectories should be mined [29] before the core process of feature clustering. Here, space-time paths of time geography [30] are used to excavate the driving characteristics (Figure 2), in which the two-dimensional coordinates represent the historical space position of the vehicle, while the vertical coordinates indicate the time of the vehicle arriving at the corresponding space position. The relationships among the speed of driving \(v_{t}\) at any time \(t\), the acceleration of driving \(a_{t}\), and the tangent slope of the inclined curve \(k_{t}\) is shown in Formulas (1) and (2). \[v_{t}=\frac{1}{k_{t}} \tag{1}\]  
\[a_{t}=\lim_{\tau\to t}\frac{\frac{1}{k_{\tau}}-\frac{1}{k_{t}}}{\tau-t} \tag{2}\]  
The total square root acceleration of the total weight \(a_{v}\) in ISO2631 is used to approximate the comfort level of the human body; it is defined as follows:  
\[a_{v}=\left(\lambda_{x}^{2}a_{x}^{2}+\lambda_{y}^{2}a_{y}^{2}+\lambda_{z}^{2}a _{z}^{2}\right)^{\frac{1}{2}} \tag{3}\]  
where \(a_{x},a_{y},a_{z},\lambda_{x},\lambda_{y},\text{ and }\lambda_{z}\), respectively, represent the square root acceleration and the direction factors in the forward, horizontal, and vertical directions of the vehicle. The comparison between the root acceleration of the total weight of different values and the comfort level of the human body is shown in Table 1.  
Figure 1: The log fuzzy C-means algorithm (LFCM) algorithm. Figure 2: Vehicle space–time path. The quality of the natural environment has a significant impact on the outcome of a user's path selection. The definition of the evaluation index is  
\[I=\sum_{i=1}^{m}I_{i}=\sum_{i=1}^{m}\frac{C_{i}}{S_{i}} \tag{4}\]  
where \(I_{i},C_{i},\text{ and }S_{i}\) represent the environmental quality index, coverage rate, and evaluation standard of the natural environment factors \(i\); \(m\) is the species number of \(i\), including the road greening rate, river distribution, and air quality; and \(I\) denotes positive correlation with the comprehensive road environment. (2) Selection of cluster center  
To minimize the clustering convergence time and avoid the local optima [31; 32] before the clustering of characteristics, we must fully utilize the human inductive ability to restrict and select the value of the initial cluster center. The following principles are adopted for selecting the time, economic value, and comfort index of the cluster center. Time clustering center: \(v\geq v_{0},|a|\geq|a_{0}|\), where \(v_{0},a_{0}\) according depend on adjustments to a specific situation. Economic cluster center: \(|a|\leq|a_{0}|,c_{0}\to c_{0\text{-min}}\), where \(c_{0}\) represents the consumption rate for oil and facilities, and \(c_{0\text{-min}}\) represents the minimum consumption rate for fuel and facilities. Comfort clustering center: \(|a_{v}|\leq|a_{v0}|,I\geq I_{0}\), where \(a_{v0}\) is routinely specified as 0.63 while \(I_{0}\) is adjusted according to user requirements. (3) Improved fuzzy C-means clustering  
The minimum element, which contains the driving characteristics, is defined as the vector eigenvalue and is called the vector element. The Hausdorff distance [33; 34; 35; 36] of each vector element to the time, economy, and comfort clustering centers is calculated, and the contribution of driving information to three different driving styles is measured. The fuzzy C-means algorithm is used to complete the clustering of the characteristics of each vector element. The concrete steps are as follows.