## 5 Discussion  
A comparative analysis was performed against several cutting-edge models frequently employed in autonomous driving and semantic segmentation tasks to evaluate the efficacy of the suggested segmentation model. The obtained results, as demonstrated in Table 6, highlight a clear progression in semantic segmentation model performance over time, as measured by the mean Intersection over Union (IoU). Earlier models, such as SegNet 4D and Q-YoloP, with mIOUs of 55.2% and 61.2%, respectively, show limited capability in accurately segmenting image regions, likely due to less effective architectures and feature extraction mechanisms. More recent approaches, including HRDLNet, DLT-Net, and Yolo-P, demonstrate moderate improvements, achieving mIOUs in the 70-73% range. These models benefit from more advanced designs and deeper networks, allowing for better context understanding. Segformer, a transformer-based model, further raises the bar with an mIOU of 75.08%, reflecting the impact of attention mechanisms in capturing long-range dependencies. The IDS model and the standard U-Net show competitive performance with mIOUs of 83.63% and 84.16%, respectively, indicating the robustness of encoder-decoder architectures in segmentation tasks. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **AUC (\%)** & **mIOU (\%)** \\ \hline Majority voting & 97.9 & 97.95 & 97.95 & 97.9 & 96 & 87.42 \\ Weighed Averaging & 97.9 & 97.95 & 97.95 & 97.9 & 96 & 88.46 \\ Proposed Hybrid & 98.94 & 98.91 & 98.93 & 98.91 & 98.4 & 89.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Result of Combined Attention in a Different Configuration. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Backbone Configuration** & **mIOU (\%)** & **Params (M)** & **FLOPs (G)** & **Inference Time (ms)** & **FPS** \\ \hline MobileNetV3 Only & 85.21 & 17.8 & 36.4 & 6.3 & 38 \\ EfficientNetB7 Only & 87.38 & 60.2 & 143.5 & 5.0 & 20 \\
**Proposed Hybrid (AEA-GAN)** & **89.46** & **39.5** & **92.1** & **3.0** & **26** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Runtime Performance. On the other hand, the reported segmentation accuracy of 98.94% on the BDD100K dataset may appear high compared to other methods. However, this metric reflects pixel-wise classification accuracy, which tends to be inflated in datasets where background pixels dominate. To address this, we also report the mean Intersection over Union (mIoU) of 89.46%, which provides a more balanced evaluation across classes. To prevent overfitting and validate generalizability:--We applied diverse data augmentation techniques (flipping, brightness variation, cropping).--Dropout and L2 weight decay were used during training. The model employed early stopping based on validation loss trends.--Ablation studies and backbone comparisons were performed to confirm consistent improvements. Furthermore, training and validation curves showed no signs of divergence, and validation metrics remained stable, confirming the robustness of the training process. These precautions collectively ensure that the high accuracy is not a result of overfitting, but rather due to the effectiveness of the AEA-GAN architecture. Notably, the proposed Adaptive Ensemble GAN significantly outperforms all existing methods, achieving an impressive mIOU of 89.46%. This suggests that the ensemble strategy and adversarial learning framework contribute to enhanced generalization and more accurate segmentation, establishing it as a state-of-the-art approach. Also, the performance across the Cityscapes and KITTI datasets, particularly with the proposed AEA-GAN model. On Cityscapes, AEA-GAN achieves a remarkable mIoU of 89.02 \(\pm\) 0.2, outperforming established state-of-the-art methods like MaskFormer (84.3 mIoU) and SegFormer (84.0 mIoU). Similarly, on KITTI, AEA-GAN attains 88.13 \(\pm\) 0.3 mIoU, surpassing Spherical Transformer (74.8 mIoU) and other leading models.