# Wheel Odometry with Deep Learning-Based Error Prediction Model for Vehicle Localization
## 3 Transformer Model
### Model's Parameter Adjustment Process  
Taking Transformer-NDC as an example, the parameter adjustment process is shown in this section. Taking the test on the V-Vfb02d dataset as an example, the four rightmost  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Driving Condition** & **IO-VNB Data Subset** & **Total Travel Time, Distance Traveled, Speed and Acceleration** \\ \hline Motorway & V-Vw12 & 1.75 min, 2.64 km, 82.6 to 97.4 km/h, \(-\)0.06 to 0.07 g \\ \hline \multirow{2}{*}{Sharp cornering and successive left} & V-Vw6 & 2.1 min, 1.08 km, 3.3 to 40.7 km/h, \(-\)0.34 to 0.26 g \\  & V-Vw7 & 2.8 min, 1.23 km, 0.4 to 42.2 km/h, \(-\)0.37 to 0.37 g \\  & V-Vw8 & 2.7 min, 1.12 km, 0.0 to 46.4 km/h, \(-\)0.37 to 0.27 g \\ \hline \multirow{4}{*}{Wet road} & V-Vfb8 & 1.2 min, 1.35 km, 60.9 to 76.5 km/h, \(-\)0.35 to 0.08 g \\  & V-Vtb11 & 0.7 min, 0.84 km, 65.1 to 75.3 km/h, \(-\)0.05 to 0.12 g \\  & V-Vtb13 & 2.1 min, 0.99 km, 7.5 to 43.3 km/h, \(-\)0.31 to 0.22 g \\ \hline \multirow{4}{*}{Quick changes in vehicle’s acceleration} & V-Vfb02e & 1.6 min, 1.52 km, 37.4 to 73.9 km/h, \(-\)0.24 to 0.19 g \\  & V-Vta12 & 1.1 min, 1.27 km, 44.7 to 85.3 km/h, \(-\)0.44 to 0.13 g \\ \hline \multirow{4}{*}{Roundabout} & V-Vta11 & 1.0 min, 0.92 km, 26.8 to 97.7 km/h, \(-\)0.45 to 0.15 g \\  & V-Vfb02d & 1.5 min, 0.84 km, 0.0 to 57.3 km/h, \(-\)0.33 to 0.31 g \\ \hline \multirow{4}{*}{Hard brake} & V-Vw16b & 2.0 min, 1.99 km, 1.3 to 86.3 km/h, \(-\)0.75 to 0.29 g \\  & V-Vw17 & 0.5 min, 0.54 km, 31.5 to 72.7 km/h, \(-\)0.8 to 0.19 g \\ \hline \multirow{4}{*}{Longer GNSS outage} & V-Vtb3 & 13.8 min, 0.71 km, 0.0 to 37.5 km/h, \(-\)0.23 to 0.33 g \\  & V-Vfb02a & 59.9 min, 96.5 km, 0.0 to 122.3 km/h, \(-\)0.5 to 0.37 g \\ \cline{1-1}  & V-Vfb02b & 18.3 min, 7.69 km, 0.0 to 84.3 km/h, \(-\)0.5 to 0.35 g \\ \hline \hline \end{tabular}
\end{table}
Table 4: IO-VNB data subsets for performance evaluation.  
columns of Table 5 list the model's corresponding performance on the four evaluation metrics. The parameter values listed for the baseline model are the final parameter values selected.  
The five sections A-E listed in Table 5 test the effect of changing the corresponding parameters on the results. A changes \(h\) with respect to the baseline, and it can be seen that the mean and maximum error increase when \(h\) decreases to 4, indicating a decrease in the accuracy and reliability of the model. When \(h\) increases to 16, although the maximum error is slightly reduced, the number of model parameters increases and the model's computational efficiency decreases, so this parameter value is not chosen. B changes \(N\) with respect to the baseline, and the maximum error increases when \(N\) decreases to 4 and increases to 8, indicating a decrease in the model's reliability, and the mean error increases when \(N\) decreases to 4, indicating a decrease in the model's accuracy. C changes \(d_{model}\) relative to the baseline, and when \(d_{model}\) decreases to 256 and increases to 1024, the maximum error increases significantly, indicating a decrease in the model's reliability, and the standard deviation increases showing that the model stability decreases. D changes \(df\) with respect to the baseline, and it can be seen that the mean and maximum error increase when \(d_{ff}\) decreases to 16. When \(d_{ff}\) increases to 64, there is a large increase in the maximum value, mean, and standard deviation of error, indicating that the reliability, accuracy, and stability of the model decrease significantly. E changes _Dropout_ relative to the baseline, and when _Dropout_ increases to 0.1 and 0.2, the maximum value, mean, and standard deviation of error increase significantly, indicating that the reliability, accuracy, and stability of the model decrease significantly.