# Monovision End-to-End Dual-Lane Overtaking Network without Map Assistance
## 3 Simulation Experiment and Result Analysis
### Ablative Study and Visualization  
**Predictor component analysis.** In this paper, the ablation experiments of several innovative points were analyzed to verify the importance of each part, and the results are shown in Table 3. Firstly, in different overtaking stages, different image features may correspond to similar safety probabilities. Since we discarded the temporal network to learn contextual information, and the number of parameters required for optimal fitting varied across different overtaking stages, the Phase Selector (PS) played a crucial role. Due to the continuous distribution of probability label values in each group of data, it was challenging to quickly distinguish the features among the data. Numerous experiments have shown that directly importing all data for training without a pre-trained model can easily lead to oscillations and failure to converge at local optima. By importing the dataset in batches based on probability labels and prioritizing highly discriminative data for training, we achieved satisfactory fitting results.  
**Velocity adaptation coefficient.** If one calculates the second derivative of the planned overtaking trajectory to derive the steering control signal sequence and then executes steering control at each moment, with a fixed velocity, the vehicle's trajectory will be quite stable. However, in reality, different situations will result in varying velocities. If the vehicle's speed is higher than the ideal speed, the traveled distance at each position will be greater, causing an increase in lateral displacement that could extend beyond the lane. Conversely, if the lateral displacement decreases, the vehicle could collide with the leading vehicle. Therefore, the introduction of the parameter \(\alpha\) can help control the variation in trajectory length while ensuring that even after a simple second-derivative calculation, the vehicle's maximum lateral displacement is maintained. According to Figure 7, it can  
Figure 6: (**a**) The trajectory of the vehicle accelerating and overtaking before decelerating and returning to the original lane, (**c**) the trajectory of the vehicle returning to the original lane when it tried to overtake and encountered danger. (**b**,**d**) show the changes in the steering wheel angle in the two cases.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ADIT** & **PS** & **Loss Function** & **Optimal Loss** & **Optimal Accuracy** \\ \hline \(\surd\) & \(\times\) & Our Loss & 0.425 & 76.6 \\ \hline \(\surd\) & \(\surd\) & BCELoss & 0.381 & 79.6 \\ \hline \(\times\) & \(\surd\) & Our Loss & 0.491 & 68.2 \\ \hline \(\times\) & \(\times\) & BCELoss & 0.488 & 53.1 \\ \hline \(\surd\) & \(\surd\) & Our Loss & **0.361** & **86.7** \\ \hline \hline \end{tabular}
\end{table}
Table 3: This paper considers a correct prediction to be obtained when the difference between the output probability and the probability label of the dataset is within 0.15.  
be seen that within the range of \(v\in[10,35]\), the maximum lateral displacement was in the range of \([2.82,3.84]\) when \(\alpha\) was introduced, while the maximum lateral displacement without introducing \(\alpha\) was in the range of \([1.04,9.23]\).