## 4 Results
### Ablation Study of Framework Components  
To validate the effectiveness of each core component in the proposed AEA-GAN architecture, we conducted a detailed ablation study. This involved systematically modifying or removing key parts of the framework to observe their individual impact on performance. First, we removed the GAN component and trained the model solely with the segmentation loss. This version (without the adversarial discriminator) led to a noticeable drop in performance, with the mIoU decreasing from 89.46% to 85.72%, demonstrating the importance of the GAN in producing realistic and refined segmentation maps. Next, we disabled the AEA module entirely. Without the self-attention, spatial attention, and channel attention branches, the model's mIoU further dropped to 84.93%. This confirms that attention mechanisms play a significant role in guiding the model to focus on relevant regions and features in the input. We then evaluated the contribution of each attention mechanism by removing it one at a time. Without self-attention, the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Backbone Architecture** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **AUC (\%)** & **mIOU (\%)** \\ \hline MobileNetV3 Only & 97.65 & 97.60 & 97.62 & 97.61 & 95.1 & 85.21 \\ EfficientNetB7 Only & 98.12 & 98.05 & 98.09 & 98.07 & 96.8 & 87.38 \\ Proposed Hybrid Model & 98.94 & 98.91 & 98.93 & 98.91 & 98.4 & 89.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison of different backbone configurations. Figure 12: Proposed method Comparison among all three datasets. model achieved 84.66% mIoU; removing spatial attention yielded 84.52%, and without channel attention, the mIoU declined slightly further to 84.49%. These results show that all three types of attention--self, spatial, and channel--contribute meaningfully to the final performance, and their combined use yields better accuracy. As shown in Table 2, we replaced the adaptive fusion in AEA with a standard, non-adaptive attention mechanism using fixed weights. This version achieved a slightly improved mIoU of 85.39% compared to using no attention at all, but still underperformed compared to the proposed AEA-GAN with dynamic weighting. This highlights the advantage of adaptively learning attention importance based on input complexity. Overall, this ablation study confirms that each component of the framework--GAN structure, attention mechanisms, and adaptive attention fusion--plays a vital role in achieving superior semantic segmentation performance in autonomous driving scenarios. The numerical results of the evaluation metrics for the Effect of Individual Attention of the proposed method are presented in Table 3. The results demonstrate its superior performance across various indicators. Spatial attention and self-attention mechanisms both achieved an accuracy of 97.9%, precision of 97.95%, recall of 97.95%, F1-score of 97.9%, and an AUC of 96. In comparison, the proposed method outperformed these approaches, achieving an impressive accuracy of 98.94%, precision of 98.91%, recall of 98.93%, F1-score of 98.91%, and an AUC of 98.4. These results indicate that the proposed method effectively integrates channel and spatial features, delivering better overall performance and robustness for vision-based tasks. To test the performance of the proposed methods, Combined Attention, Majority Voting, and Weighted Averaging were used, which are two commonly used ensemble techniques in machine learning to combine the outputs of multiple models to make a final decision or prediction. As shown in Table 4, the Majority Voting and Weighted Averaging configurations achieved similar performance, with an accuracy of 97.9%, precision and recall of 97.95%, F1-Score of 97.9%, and AUC of 96%. The proposed Method significantly outperformed these configurations.