# Study on Path Planning Method for Imitating the Lane-Changing Operation of Excellent Drivers
## 4 Path Planning Method Based on Excellent Driver Lane-Changing Model
### Excellent Driver Lane-Changing Model  
The lane-changing trajectories chosen by different drivers under the same working conditions will be different due to the various driving habits and personalities of each driver. Figure 7 shows the lane-changing paths of different drivers at the same speed. It can be observed that the driving characteristic is one of the most important factors that influence the travel route. Through the analysis of the operating habits of different drivers, the driver types are divided into aggressive, intermediate and conservative. In this research, 1 represents aggressive type, 0 represents conservative type, and 0.5 means intermediate type. Figure 8 shows the paths of the free lane change and the obstacle avoidance lane change. In the figure, it is shown that different steering intentions also create a considerable impact on the vehicle driving path. In this paper, 1 represents the intention of obstacle avoidance lane change and 0 is for free lane change. In addition, the speed and the distance from obstacle also have an impact on the choice of the lane-changing path. Figure 9 shown the lane-changing paths of one driver at different speeds. Therefore, the excellent driver lane-changing model in this paper has four inputs, namely driver type, steering intention, vehicle speed and distance from obstacle. According to the above analysis, the optimal lane-changing path under different conditions can be  
Figure 6: Flow chart of GA-BP neural network algorithm.  
obtained when the characteristic point coordinates (\(x_{m}\), \(y_{m}\)) and the final distance \(x_{f}\) of lane-changing are determined. Thus, the model proposed in the paper has three outputs: \(x_{m}\), \(y_{m}\), and \(x_{f}\).  
The experimental data are shown in Table 2. The steering intention is determined based on distance from obstacle. The distance from obstacle under free lane change condition is set to 100 m. Therefore, there are 300 experimental data. Ninety percent of the data were selected at random as the training data, while the remaining are the testing data.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Classification** & **Information** \\ \hline Number of drivers & 5 \\ Velocity (km/h) & 30, 35, 40, 45, 50 \\ Distance from obstacle (m) & 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 100 (no obstacle) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Information of experiment data.  
Figure 8: The lane-changing paths with different intention.  
Figure 7: The lane-changing paths of different drivers at the same speed.  
Figure 9: The lane-changing paths of one driver at different speeds.  
The effectiveness of the neutral network was estimated using the GA-BP error. The error is defined as the difference between the simulation output and experimental output as Equation (17). For the testing in this research, the error is shown in Figure 10. In the figure, it can be observed that the GA-BP neural network is with high accuracy.  
\[error=output\_simu-output\_testing \tag{17}\]  
where output_simu is the output value of the GA-BP neural network and output_testing is the value of the testing date.  
Figure 11 shows the mean square error variation curve of the GA-BP neural networks model. The mean square error is 0.009 after training, and it can meet the precision requirements.