# Reinforcement Learning Decision-Making for Autonomous Vehicles Based on Semantic Segmentation
## 3 Experimental Procedure and Results
### Simulation Results and Discussion  
#### 3.5.1 Semantic Segmentation Network Training Curve Results  
The evaluation of semantic segmentation focused on the accuracy and mean intersection over union (MIOU) metrics [22]. Accuracy measured segmentation precision, defined as the ratio of correctly classified pixels to the total number of pixels; the higher this ratio, the better the segmentation performance. MOU evaluated the segmentation accuracy and completeness for each category by calculating the ratio of the intersection to the union between predictions and ground truth labels. Together, these two metrics provided a quantitative basis for assessing the performance of semantic segmentation models. As seen in Figure 5(a), accuracy showed an upward trend before 20,000 iterations, indicating that the model gradually improved its prediction accuracy for pixel categories. Although there were some fluctuations, the overall trend was upward, which indicated that the model's overall segmentation accuracy was improving. After 20,000 iterations, the accuracy curve stabilized, with values ranging between 0.8 and 1, demonstrating that the model had reached a stable classification capability on the current training data. Figure 5(b) shows that the MIOU curve gradually increased during training, indicating that the model's segmentation performance continuously improved. Combining the trends of accuracy and MIOU, it was evident that the model had achieved good training results.  
In Figure 7, the left side showed the input test set images, while the right side displayed the generated prediction images. From the results, it could be observed that the semantic segmentation model effectively simplified, extracted, and categorized the predefined elements of semantic category scenes.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Training Parameters** & **Numerical Value** \\ \hline \multirow{5}{*}{Semantic segmentation parameter settings} & Batch size & 4 \\ \cline{2-3}  & Minimum scaling factor & 0.5 \\ \cline{2-3}  & Maximum scaling factor & 2.0 \\ \cline{2-3}  & Scaling step & 0.25 \\ \cline{2-3}  & Number of iterations & 70,000 \\ \hline \multirow{5}{*}{RL algorithm parameter settings} & Learning rate & 0.0001 \\ \cline{2-3}  & Batch size & 64 \\ \cline{2-3}  & Discount factor & 0.99 \\ \cline{2-3}  & Training set length & 7500 \\ \cline{2-3}  & Number of training episodes & 200,000 \\ \hline DDQN & Target network update frequency & 5 \\ \hline \multirow{5}{*}{PPO} & Exploration noise & 0.2 \\ \cline{2-3}  & Decay rate & 0.05 \\ \cline{1-1} \cline{2-3}  & Minimum exploration noise & 0.05 \\ \hline \end{tabular}
\end{table}
Table 2: Simulation experiment parameter setting.  
Through the training process and results of the semantic segmentation model (DeepLabv3P), this paper identified several challenging categories and scenarios. These failure modes included pedestrians, bicycles, and distant vehicles, which were often misclassified or overlooked due to their small pixel representation in semantic label maps. Consistently segmenting moving vehicles and pedestrians proved difficult, especially in high-density traffic or when objects moved at high speeds. Overlapping or visually similar classes (e.g., curbs and sidewalks) occasionally caused boundary misclassification. Rain and low-light conditions reduced the visibility of lane markings and small obstacles, leading to segmentation errors. In traffic-dense urban intersections, the network sometimes struggled to segment traffic signs or partially occluded objects, such as pedestrians behind vehicles. These issues mainly stemmed from class imbalance, limited spatial resolution, and the domain gap between synthetic training data and real-world variability.