# A Vehicle Velocity Prediction Method with Kinematic Segment Recognition
## 2 Data Preprocessing
### Kinematic Fragment Feature Parameter Extraction  
The kinematic segment refers to the driving velocity information between two adjacent stopping points [25]. There are many different kinematic segments in the same driving condition, and there are many similar kinematic segments in different driving conditions. In the short time domain, the kinematic segment can better reflect the driving characteristics of the vehicle, so the kinematic segment is considered as a reference for velocity prediction. The extracted kinematic segments are clustered together according to the driving characteristic parameters, and used as samples to train the neural network velocity prediction sub-model, because a certain cluster composed of kinematic segments with similar characteristics can master and express the characteristics and rules of a certain class of working conditions more accurately. The current driving condition is identified according to the past driving information, and the corresponding sub-neural network is used to predict the vehicle velocity.  
The sample acquisition process is shown in Figure 2. The kinematic segments of typical driving conditions are extracted, and a multi-feature kinematic segment clustering method based on principal component analysis is proposed. The multi-feature parameters of the kinematic segments were reduced dimensionality according to the principal component analysis method, and the kinematic segments with similar feature parameters were clustered together by the K-means algorithm to obtain the training samples of the sub-models of velocity prediction under three working conditions to prepare for the subsequent velocity prediction.  
In the relevant literature, the number of characteristic parameters of the vehicle driving conditions is generally set as 10-18 [26]. Considering the calculation amount and accuracy comprehensively, the number of characteristic parameters of the kinematic segment is 10. The expression of 10 characteristic parameters is expressed as follows:  
\[v_{ave}=(\int vdt)/t \tag{1}\]  
\[v_{\max}=\max\{v_{1},v_{2},v_{3}\ldots v_{n}\} \tag{2}\]  
\[\sigma=\bigg{[}\frac{1}{n-1}{\sum_{i=1}^{n}{(v_{i}-v_{ave})^{2}}}\bigg{]}^{ \frac{1}{2}} \tag{3}\]  
\[\eta_{a}=\frac{t_{a}}{T} \tag{4}\]  
\[\eta_{d}=\frac{t_{d}}{T} \tag{5}\]  
\[a_{\max}=\max\{a_{1},a_{2},a_{3}\ldots a_{n}\} \tag{6}\]  
\[a_{\min}=\min\{a_{1},a_{2},a_{3}\ldots a_{n}\} \tag{7}\]  
\[a_{a\_ave}=\bigg{(}\int\frac{dv}{dt_{a}}dt\bigg{)}/\bigg{(}\int t_{a}dt\bigg{)} \tag{8}\]  
\[a_{d\_dev}=\bigg{(}\int\frac{dv}{dt_{d}}dt\bigg{)}/\bigg{(}\int t_{d}dt\bigg{)} \tag{9}\]  
\[S=\int v_{\text{car}}dt \tag{10}\]  
Figure 2: Sample acquisition flowchart.  
where \(v_{ave}\) is the average of velocity value, \(v_{\max}\) is the maximum velocity value, \(\sigma\) is the standard deviation of velocity, \(t_{a}\) is the acceleration time of each motion segment, \(T\) is the segment time, \(t_{d}\) is the deceleration time of each motion segment, \(\eta_{a}\) is the ratio of the acceleration time to the length of the segment time, \(\eta_{d}\) is the ratio of the deceleration time to the length of the segment time, \(a_{\max}\) is the maximum acceleration, \(a_{\min}\) is the minimum acceleration, \(a_{a\_{ave}}\) is the value of average deceleration, \(a_{d\_{ave}}\) is the value of average deceleration, and \(S\) is the mileage of the kinematic segment.  
A program was written in MATLAB 2018a to extract the characteristic parameters of 317 kinematic segments under 15 typical driving conditions, and some of the results are shown in Table 1.