## 5 Discussion
These results highlight AEA-GAN's robustness as a hybrid method, likely due to its effective integration of adversarial training and attention mechanisms, which enables superior feature extraction and contextual understanding compared to purely transformer-based (e.g., MaskFormer) or CNN-based (e.g., HRNet + OCR) approaches. The consistency of AEA-GAN's performance, indicated by low standard deviations (\(\pm\)0.2-0.3), further underscores its reliability. In contrast, while transformer-based models like SegFormer and MaskFormer excel on Cityscapes, their performance gaps on KITTI (e.g., SegFormer's \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Studies** & **Model** & **mIOU (\%)** \\ \hline \multirow{8}{*}{BDD100K} & [PERSON] et al. (2024) [13] & SegNet 4D & 55.2 \\  & [PERSON] et al. (2023) [15] & Q-YoloP & 61.2 \\  & [PERSON] et al. (2024) [19] & HRDLNet & 70.4 \\  & [PERSON] et al. (2019) [21] & DLT-Net & 71.3 \\  & [PERSON] et al. (2022) [23] & Yolo-P & 72.6 \\  & [PERSON] et al. (2021) [25] & Segformer & 75.08 \\  & [PERSON] et al. (2023) [16] & IDS model & 83.63 \\  & Standard U-Net model & Standard U-Net & \(84.16\pm 0.18\) \\  & **Proposed hybrid method** & **AEA-GAN** & \(\mathbf{89.46\pm 0.1}\) \\ \hline \multirow{8}{*}{Cityscapes} & [PERSON] et al. (2021) [36] & MaskFormer & 84.3 \\  & [PERSON] et al. (2021) [25] & SegFormer & 84.0 \\  & [PERSON] et al. (2020) [37] & HRNet + OCR & 81.1 \\  & [PERSON] et al. (2019) [38] & EDANet & 67.3 \\  & [PERSON] et al. (2021) [39] & STDC-Seg75 & 75.3 \\  & [PERSON] et al. (2023) [40] & PIDNet-5 & 78.6 \\  & [PERSON] et al. (2021) [41] & DDRNet-23 & 80.6 \\  & **Proposed hybrid method** & **AEA-GAN** & \(\mathbf{89.02\pm 0.2}\) \\ \hline \multirow{8}{*}{KITTI} & [PERSON] et al. (2019) [28] & VPLR & 72.8 \\  & [PERSON] et al. (2022) [42] & 2 DPASS & 72.9 \\ \cline{1-1}  & [PERSON] et al. (2023) [43] & Spherical Transformer & 74.8 \\ \cline{1-1}  & **Proposed hybrid method** & **AEA-GAN** & \(\mathbf{88.13\pm 0.3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance Comparison with Existing Studies. 75.08 mIoU adaptation) suggest challenges in generalizing to datasets with sparser annotations or varied environments. Real-time models (e.g., 2 DPASS, VPLR) trade accuracy for speed, but AEA-GAN's hybrid design appears to bridge this gap, achieving high accuracy without compromising efficiency. These advancements position AEA-GAN as a versatile solution for autonomous driving tasks, though further validation on larger datasets (e.g., BDD100K) and edge-device deployment tests is needed to assess scalability. The results also emphasize the importance of hybrid architectures in pushing the boundaries of semantic segmentation beyond the limitations of single-paradigm models. [PERSON] [44] demonstrated the challenge of misidentifying splashed water or hail as vehicles, which remains a significant limitation for vision-based driving systems. Our approach addresses this issue by employing an Adaptive Ensemble Attention mechanism that focuses on semantically consistent regions and dynamically filters out contextually irrelevant noise. Although our dataset (BDD100K) does not provide explicit splash or hail annotations, the model's performance on scenes with rain, reflections, and occlusions indicates improved resilience to such anomalies. Although this study demonstrates promising results, it is important to acknowledge several limitations that may guide future research. First, the model's performance may deteriorate under challenging conditions involving noisy inputs, such as images affected by motion blur, sensor noise, or other artifacts. While the architecture performs robustly in controlled settings, it has not been comprehensively evaluated under such real-world variability. Second, the model exhibits potential limitations in detecting rare or under-represented classes due to imbalanced training data.