# Estimation of Skid-Steered Wheeled Vehicle States Using STUKF with Adaptive Noise Adjustment
## 5 Experiment Results and Analysis
### Turning Driving Condition at High Speed  
In this experimental test, the vehicle drives at a S-shaped trace at the speed of 30 km/h. The trace is shown in Figure 15. The vehicle turns in a large radius.  
Figure 16 shows the longitudinal state of the vehicle when it turns. The two estimation algorithms show almost no difference when the longitudinal velocity changes slightly. The longitudinal slip rate is relatively small, and the symbol of left and right slip rate alternately changes. This reflects that the two-side tires alternately are skidding and slipping. The estimation results correspond to the vehicle trace.  
The lateral velocity, yaw rate, and tire sideslip angle estimation results are shown in Figure 17. The two estimation algorithms perform almost similarly when observing yaw rate because yaw rate is the measurement value.  
Figure 16: The longitudinal state when a skid-steered wheeled vehicle turns in a large radius.  
Figure 15: The moving trace of the vehicle at the speed of 30 km/h.  
The estimated lateral velocity from AN-STUKF performs with better accuracy than the traditional UKF, in most time periods. However, sometimes the traditional UKF performs better than AN-STUKF. The possible reason is that the linear adjustment algorithm of variance matrix cannot satisfy all conditions.  
The sideslip angle of tires in the large radius condition is relatively small as compared with the small radius condition.  
The sideslip angle of the vehicle body estimating result shown in Figure 18 shows similar characteristics as that of the lateral velocity estimating result. The sideslip angle is relatively small, so the vehicle condition is regarded to be steady.  
Table 3 shows the error analyses in small radius turning driving condition for the two estimations. The relative error used the data from 20 to 25 s to avoid the influence of values close to zero. The proposed AN-STUKF performance does not have much advantage as compared with the traditional UKF in all error evaluation indexes when the vehicle does not drive in high-nonlinear conditions.  
Figure 17: The lateral state when a skid-steered wheeled vehicle turns in a large radius.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & **Max Absolute Error** & **Max Relative Error** & **Mean Absolute Error** & **Mean Relative Error** & **Standard Deviation** \\ \hline UKF & 0.0278 & 145.71\% & 0.0090 & 34.05\% & 0.0108 \\ AN-STUKF & 0.0285 & 119.35\% & 0.0085 & 25.63\% & 0.0105 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Error analyses for S-shape trace.  
Figure 18: The sideslip angle of vehicle body.  
The ICRs shown in Figure 19 in this condition show abnormity when the yaw rate approaches zero, because the effect of error increases when the real state value is small. Thus, in this section, we cut out the data from 4 to 10 s to analyze the ICRs. The ICRx estimating result shows that, even though the turning radius is small, the ICRs would show the phenomenon of shifting forward when the speed is high enough. The experiment verifies the critical speed of ICRs moving forward proposed in [13].