# Monovision End-to-End Dual-Lane Overtaking Network without Map Assistance
## 3 Simulation Experiment and Result Analysis
### Comparison with Existing Methods
** Traditional path planning involves first planning trajectory points and then executing trajectory tracking control. This approach offers the advantage of better robustness during the planning process, making it suitable for most autonomous driving scenarios. However, the scenarios addressed in this experiment involved relatively straight \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**L2 (m)**} & \multirow{2}{*}{\begin{tabular}{c} \\ **3 s** \\ \end{tabular} } & \multicolumn{1}{c}{**5 s**} & \multirow{2}{*}{\begin{tabular}{c} **\(M_{t}\)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **\(M_{o}\)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **\(M_{s}\)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **\(P_{c}\)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **\(T_{c}\)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Route** \\ **Completion** \\ \end{tabular} } \\ \hline EMPlanner [2] & 0.291 & 1.853 & 4.134 & 8.2 & 1.09 & 72.6 & 0.73 & 194.5 & **99.4** \\ \hline TCP [21] & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & 0.84 & 215.6 & 97.1 \\ \hline Cooperation [26] & 0.285 & 1.977 & 4.066 & 11.2 & 6.8 & 78.1 & **0.58** & 159.4 & 99.2 \\ \hline Global Info [26] & 0.279 & 2.011 & 4.153 & 11.4 & 6.5 & **84.3** & 1.47 & 182.8 & 98.3 \\ \hline KF Control [27] & 0.314 & **1.799** & 3.976 & 10.7 & 33.5 & 63.4 & 2.88 & 198.5 & 89.0 \\ \hline GDCP & **0.276** & 1.919 & **3.906** & **5.2** & **3.2** & 81.7 & 0.71 & **147.4** & 98.7 \\ \hline GDCP-OFP & 0.289 & 1.936 & 4.011 & 5.4 & 7.9 & 62.3 & 2.42 & 164.2 & 94.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Track matching metric. The table shows the difference between the vehicle trajectory and the ideal trajectory within three seconds from the overtaking action when the vehicle speed was 20 m/s. It also includes a comparison of lane occupation time, number of attempts, and overtaking success rate when performing overtaking tasks, as well as the collision rate, road segment completion rate, and time per kilometer when driving on a straight road. Figure 4: The output of the OFP. The left side of each row is an average of three pictures captured in a certain overtaking scenario. The right side of each row shows the fitting performance of the predictor under different scenarios. In most cases, the predicted probability was close to and generally lower than the label probability. forward route options, encompassing only three situations: following a vehicle, overtaking, and returning to the original lane when danger is detected. The results of the experiment indicated that employing a method that directly plans control signals with varying maximum lateral displacements can effectively complete these tasks. This approach not only reduces the computational complexity but also ensures the smoothness of both the trajectory and control signals, ultimately enhancing passenger comfort. [PERSON] et al. [4] built an overtaking environment with a mini car and recorded the overtaking track and the steering wheel angle signal. As one of the rare approaches that utilize common function curves for overtaking trajectory planning, our method was compared to the abovementioned method, and the trajectory outcomes are illustrated in Figure 5. Assuming that the speeds of the leading vehicle and the ego vehicle were constant, and using the geometric curve to plan the overtaking path, the route planned by the model in this paper was smoother, and the fluctuation of the control signal was not severe. This was because [PERSON] et al. 's solution was to plan the trajectory first and then use the PID algorithm to restore the planned trajectory as much as possible. Our solution was to directly calculate the executed control signal based on the derivative of each key point of the target curve, so that both the control signal and the trajectory looked smoother.