## 6 Result Analysis
### Experiment Analysis  
#### 6.2.1 The Double-Lane Change Experiment  
The sampling time interval of the GPS/RTK module is set to 0.05 s, and the sampling time of the INS is set to 0.2 s. Figure 7a shows the steering wheel angle change curve sampled by the angle sensor. Figure 7b,c are the longitudinal acceleration curve and lateral acceleration curve sampled by the INS module, and the wheel velocity curves are sampled by the Hall sensor, as shown in Figure 7d,e. The measurement data such as the longitudinal/lateral acceleration and yaw rate are transmitted to the built-in GHCKF and AGHCKF algorithm platforms of the controller, and the longitudinal velocity, lateral velocity, yaw rate and road adhesion coefficient are calculated and compared with the measured values. In Figure 7f-h, the blue line represents the direct and indirect measurement value of the sensor, the red line represents the estimated value of the GHCKF, the green line represents the estimated value of the AGHCKF, and the purple line represents the estimated value of the CKF. In Figure 7i, the blue line represents the upper limit of the road adhesion coefficient, and the pink line represents the lower limit. The longitudinal acceleration fluctuates in a small range at the 0 scale line. The longitudinal acceleration curve, the lateral acceleration curve, and the two wheel speed curves change with the law of the double-lane change. The above curves fluctuate with the vehicle movement, and their changing rule is consistent with the theoretical analysis and conforms to the actual working conditions. As shown in Figure 7f and Table 3, the AGHCKF, GHCKF and CKF algorithms can effectively track the longitudinal velocity of the vehicle and can effectively filter; the AGHCKF has the best overall effect, followed by the GHCKF and CKF. When the AGHCKF algorithm is used to estimate the lateral velocity, Figure 7g shows that the matching degree between the AGHCKF and the simulation curves is better than that for the GHCKF and CKF, and the accuracy is also the highest, as shown in Table 3. According to Figure 7h, the GHCKF, GHCKF and CKF can all accurately track the measured yaw rate. The locally enlarged image and RMSE values show that the filtering effect and estimation accuracy of the AGHCKF are higher than those of the GHCKF, and that those of the GHCKF are higher than those of the CKF. Figure 7i,j show the estimation curves of the road adhesion coefficient on the rear wheel drive side. Due to accumulation of the accuracy errors of the vehicle model, the errors of Magic Formula, the accuracy errors of the Duggoff tire model, and the errors of accuracy errors of measurement sensors, none of the algorithms cannot accurately estimate the road adhesion coefficient; they can only roughly estimate it within the coefficient range. The RMSE values are shown in Table 3, although AGHCKF\({}_{\text{RMSE}}\)\(<\) CKFRMSE\({}_{\text{RMSE}}\), the value estimated via the AGHCKF is more in the range of road adhesion coefficient than GHCKF, and that of the the GHCKF is more than the CKF. The response time of the three algorithms is similar. The curve of the AGHCKF is smoother than the GHCKF, and the curve of the GHCKF is smoother than the CKF. Overall, the AGHCKF has the best estimation effect, followed by the GHCKF, and the CKF is the worst. Figure 7: _Cont._  
Figure 7: The results of the double-lane change experiment. (**a**) The steering angle; (**b**) the longitudinal acceleration yaw rate; (**c**) the lateral acceleration; (**d**) the left rear wheel speed; (**e**) the right rear wheel speed; (**f**) the longitudinal velocity; (**g**) the lateral velocity; (**h**) the yaw rate; (**i**) the left rear wheel adhesion coefficient; and (**j**) the right rear wheel adhesion coefficient.