## 6 Result Analysis
### Experiment Analysis
#### 6.2.2 The Slalom Experiment  
The low-speed slalom experiment was conducted on the asphalt-gravel joint road, and the sensor settings are the same as those in the double-lane change experiment. Figure 8a shows the steering wheel angle change curve sampled by the angle sensor. Figure 8b,c, respectively, show the lateral acceleration and longitudinal acceleration curves sampled by the INS module, and the wheel speed curves sampled by the Hall sensor, as shown in Figure 8d,e. For the first 12 s, when the vehicle was driving on an asphalt road, the measurement data were relatively stable. After 12 s, when the vehicle was driving on the gravel road surface, the data fluctuated significantly, which is in line with the actual working conditions of the joint road. We input the measurement data into the controller, and the GHCKF and AGHCKF algorithms in the controller calculate the longitudinal velocity, lateral velocity, yaw rate, and road adhesion coefficient, and then compare them with the measured values. The line type settings in the figures are the same as those in the double-lane change experiment. As shown in Figure 8b-d, the results show that the longitudinal acceleration fluctuates within a small range of the 0 scale line, and the curves of the longitudinal acceleration, the lateral acceleration, and wheel speed change along with the slalom law. The experiment curves fluctuate with the vehicle's movement, and their changing rule is consistent with the theoretical analysis and practical working conditions. As shown in Figure 8f, the GHCKF and CKF algorithms can effectively track the longitudinal velocity of the vehicle up until 12 s, but the error relatively increases in the latter seconds. The maximum estimated value of the GHCKF reaches 11.4 m/s, while the minimum estimated value of CKF reaches 10.7 m/s; according to Table 4, the accuracy of the GHCKF is much higher than that of the CKF. However, the AGHCKF algorithm can accurately follow the longitudinal velocity measurement values of the entire vehicle throughout the entire process, the filtering effect is significant, and the AGHCKF has the highest estimation accuracy. As shown in Figure 8g, compared with the CKF, the AGHCKF and GHCKF can more accurately track lateral velocity measurements, especially in the peak and valley of the curve, and during the running time after 10 s. The AGHCKF algorithm has a better filtering effect and a higher estimation accuracy than those of the GHCKF. In the above aspects, the GHCKF is again better than the CKF; as shown in Figure 8h, the AGHCKF, GHCKF, and CKF can all accurately track the measured values for the yaw rate. The local enlarged image and Table 4 show that the AGHCKF algorithm has the best filtering performance and highest estimation accuracy. The factors affecting the estimation of the adhesion coefficient are the same as those in the double-lane change experiment, and the adhesion coefficient can only be roughly estimated within the coefficient range. The response time of the AGHCKF in the figure is about 6-7 s, the response time of the GHCKF is 6-8 s, and the response time of the CKF is about 7-10 s. The curve of the AGHCKF is smoother than the GHCKF, and the curve of GHCKF is smoother than the CKF. The RMSE values are shown in Table 4, for the adhesion coefficient of the left rear wheel: \(\text{CKF}_{\text{RMSE}}<\text{AGHCKF}_{\text{RMSE}}<\text{GHCKF}_{\text{RMSE}}\), and for the adhesion coefficient of the right rear wheel: \(\text{AGHCKF}_{\text{RMSE}}<\text{GHCKF}_{\text{RMSE}}<\text{CKF}_{\text{RMSE}}\). Overall, compared to the GHCKF and CKF, the adhesion coefficient estimated values of the AGHCKF algorithm are relatively stable, both located in the upper and lower limits. And the AGHCKF algorithm responds the fastest of all. Figure 8: _Cont._Figure 8: The results of the slalom experiment.