## 4 Experiment and Evaluation
### Actual Car Experiment
Figure 18: Tracking the effect of parallel parking. Figure 19: Predicted values compared to the skilled driver driving data: (**a**) Steering wheel turning angle curve. (**b**) Vehicle speed curve. Figure 20: Standard parking position. The mean and extreme values of 1080 groups of parking data are shown in Table 5, where \(x\) is the absolute \(x\)-axis error of the car compared to the standard parking space after parking is completed, \(y\) is the absolute \(y\)-axis error of the vehicle compared to the standard parking space after parking is completed, \(\mathbf{\theta}\) is the absolute angle error of vehicle car compared to the standard parking space after parking is completed, and time is the time spent by the car from the completion of the parking tour to the time spent in the parking space. From the table, we can learn that, compared with the current path-tracking intelligent parking model, end-to-end intelligent parking of parking in a small part of the premise of the improvement, the average value of the parking completion time is about 20 s. In the environment of fuzzy slot lines, the longest parking process is completed within 27 s. However, the average completion time for the intelligent path-tracking parking is more than 29 s, so the end-to-end intelligent parking model built in this paper has a significant advantage in terms of the completion time for parking. As shown in Figure 21, the curve environment is \"Vehicle obstacle and Columnar obstacle and Curbstone obstacles,\". The curve data are the comparison data of 20 sets of traditional trajectory tracking intelligent parking and end-to-end intelligent parking in this environment. It can be observed that end-to-end intelligent parking is more efficient than path-tracking intelligent parking, and end-to-end intelligent parking has obvious advantages in terms of parking accuracy. Based on the above data comparison, it can be concluded that the end-to-end intelligent parking model has some improvements in terms of the actual time and accuracy compared to the existing path-tracking intelligent parking model. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Environment**} & \multicolumn{5}{c}{**End to End**} & \multicolumn{5}{c}{**Path Tracking**} \\ \cline{2-10}  & & **x/m** & **y/m** & **\(\mathbf{\theta}\)/(\({}^{\circ}\))** & **Time/s** & **x/m** & **y/m** & **\(\mathbf{\theta}\)/(\({}^{\circ}\))** & **Time/s** \\ \hline \multirow{2}{*}{Vehicle obstacle, columnar obstacle, columnar obstacle, and curbstone obstacles} & Max & 0.275 & 0.336 & 0.772 & 17.20 & 0.386 & 0.528 & 0.966 & 59.80 \\ \cline{2-10}  & Min & 0.015 & 0.094 & 0.107 & 14.63 & 0.132 & 0.152 & 0.413 & 21.12 \\ \cline{2-10}  & Mean & 0.151 & 0.177 & 0.531 & 15.78 & 0.212 & 0.229 & 0.614 & 31.76 \\ \hline \multirow{2}{*}{Columnar obstacle and curbstone obstacles} & Max & 0.436 & 0.413 & 0.906 & 18.02 & 0.781 & 0.758 & 1.760 & 56.43 \\ \cline{2-10}  & Min & 0.081 & 0.087 & 0.187 & 14.38 & 0.138 & 0.197 & 0.571 & 29.14 \\ \cline{2-10}  & Mean & 0.216 & 0.231 & 0.376 & 15.60 & 0.237 & 0.376 & 0.912 & 39.96 \\ \hline \multirow{2}{*}{Vehicle obstacle and curbstone obstacles} & Max & 0.381 & 0.289 & 0.945 & 19.78 & 0.513 & 0.562 & 1.593 & 62.03 \\ \cline{2-10}  & Min & 0.076 & 0.102 & 0.536 & 15.47 & 0.073 & 0.109 & 0.618 & 37.88 \\ \cline{2-10}  & Mean & 0.173 & 0.163 & 0.691 & 16.03 & 0.210 & 0.292 & 0.811 & 45.17 \\ \hline \multirow{2}{*}{Vehicle obstacle and fuzzy parking slot line} & Max & 0.623 & 0.574 & 0.981 & 20.69 & 0.593 & 0.813 & 1.263 & 46.27 \\ \cline{2-10}  & Min & 0.207 & 0.146 & 0.391 & 17.31 & 0.137 & 0.069 & 0.294 & 37.64 \\ \cline{2-10}  & Mean & 0.278 & 0.224 & 0.502 & 19.76 & 0.301 & 0.326 &