## 3 Experimental Results and Analysis
### Experimental Results Analysis  
In the driving intention classification, 20 groups of vehicle operation data, totaling 20,238 sample points, were randomly selected for testing. The average recognition accuracy of vertical driving intention is 94.83%, and the average recognition accuracy of horizontal driving intention is 93.25%, which has a good recognition effect on driving intention. Figure 2 shows the driving intention classification results from one set of vehicle data, with different colors representing different driving intentions in the horizontal and vertical directions. The results of the multistep prediction of future velocity using the iterative method are shown in Figure 3, and the graphs show the comparison curves of velocity prediction results for the third, fifth, and tenth steps of the prediction. From the figure, it can be seen that the error of velocity prediction increases with the increase in the prediction step length. Secondly, in the case of drastic speed changes, the other two methods have more aggressive prediction results than the algorithm used in this paper, and the difference is more obvious in the case of frequent acceleration and deceleration. The GPR prediction curve based on FCM is closer to the real speed of the vehicle in front, and the prediction error is significantly reduced. Figure 2: Driving intent classification results: (**a**) vertical driving intention classification results; (**b**) horizontal driving intention classification results. The results of multistep prediction for heading angle are shown in Figure 4 for the leading car, respectively, and the graphs show the prediction curves of heading angle for the third step length, the fifth step length, and the tenth step length. As seen from the graph, first of all, with the increase in the prediction step, each algorithm's prediction results will appear with corresponding lag. The more prediction steps there are, the more significant the lag. Secondly, the prediction results of the other two methods are more aggressive than those of the algorithm adopted in this paper, which leads to an increase in the prediction error, especially in the course change operation; where the course angle changes more in a short period, the difference of the prediction results of the three methods is more obvious. The linear stability model based on FCM is more similar to the actual angle of attack of the vehicle in front of it, and the prediction error is reduced. Figure 3: Comparison of multistep velocity prediction results: (**a**) comparison of the results of 3-step speed prediction; (**b**) comparison of the results of 5-step speed prediction; (**c**) comparison of the results of 10-step speed prediction. As shown in Figure 5, the comparison of RMSE and MAE of prediction in horizontal and vertical coordinates in the third step, fifth step, and tenth step using different prediction methods. Firstly, the three methods' RMSE and MAE increased with the prediction step's increase, especially in the tenth step. The RMSE and MAE values of the prediction algorithm without driving intent classification increased significantly in the horizontal coordinate compared to the fifth step, which proved that the prediction algorithm without driving intent classification was less effective at longer steps. Second, the RMSE and MAE values of the algorithm in this paper, both lower than the other two algorithms, prove the effectiveness of this algorithm. Figure 4: Comparison of multistep heading angle prediction results: (**a**) comparison of the results of 3-step heading angle prediction; (**b**) comparison of the results of 5-step heading angle prediction; (**c**) comparison of the results of 10-step heading angle prediction. The trajectory prediction results of this algorithm are compared with the actual trajectory as shown in Figure 6. A 95% prediction error is less than 0.013 m for the tenth step horizontal distance.