## 3 Proposed Method
Tables 2 and 3 present the quantitative results of the ablation study. We found that the FPL module in DriveSegNet improved the mIoU by 0.72% on the Cityscapes dataset. After incorporating the PCAM and LAFM modules, segmentation accuracy increased, but the inference speed decreased. Using all three proposed modules, DriveSegNet achieved an mIoU of 77.33% and 70.5 FPS. Compared to the baseline model, the mIoU improved by 2.36%. On the CamVid dataset, after adding the three modules, the mIoU increased by 1.58% compared to the baseline model. Therefore, the proposed module is effective in enhancing semantic segmentation and making visual differences more obvious. ### Comparison Experiment

In this study, we compared DriveSegNet with several existing semantic segmentation methods, particularly focusing on real-time processing capabilities and accuracy in autonomous driving scenarios. The real-time requirements of autonomous driving systems make processing speed a critical performance metric, especially in multi-lane, complex traffic environments, where efficient semantic segmentation can significantly improve the system's responsiveness to its surroundings. Specifically, the experimental results on the Cityscapes dataset show that DriveSegNet achieves processing speeds of 102.5 FPS (DriveSegNet-1) and 70.5 FPS (DriveSegNet-2) while maintaining high segmentation accuracy. This speed is crucial for real-time feedback in autonomous driving systems, enabling rapid responses to changing traffic conditions. Therefore, DriveSegNet holds significant practical value in autonomous driving and similar applications, particularly in complex road environments where fast reactions are essential for accurate scene understanding and decision-making. #### 4.4.1 Cityscapes Dataset Comparison

DriveSegNet was compared with various semantic segmentation algorithms, including Efficient Residual Factorized Network (ERFNet) [43], Deep Feature Aggregation Network-A Version (DFANet-A) [44], SwiftNet with ResNet-18 backbone (SwiftNetRN-18) [45], Efficient Segmentation Network (ESNet) [46], Lightweight Encoder-Decoder Network (LEDNet) [47], Transformer-Driven Deep Learning for Bi-segmented 18-layer Network (TD\({}^{4}\)-Bise18) [48], Hyper-resolution Semantic Segmentation Model (HyperSeg-M) [49], and Hyper-resolution Semantic Segmentation Small Model (HyperSeg-S) [49]. Table 4 compares DriveSegNet's accuracy and speed with other methods on the Cityscapes datasets. Unreported metrics are marked as '--'. While some models were reproducible under our RTX 3090 setup, we excluded these results due to undefined original evaluation criteria, ensuring fair comparison. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **FPL** & **PCAM** & **LAFM** & **mIoU (\%)** & **FPS** \\ \hline Baseline & & & & 74.97 & 110.1 \\ DriveSegNet-1 & ✔ & & & 75.69 & 98.1 \\ DriveSegNet-1 & ✔ & ✔ & & 76.96 & 97.8 \\ DriveSegNet-1 & ✔ & & ✔ & 75.86 & 87.6 \\ DriveSegNet-1 & ✔ & ✔ & ✔ & 77.33 & 70.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation study of the proposed modules on the Cityscapes validation set. ✔ denotes the presence or inclusion of the component. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **FPL** & **PCAM** & **LAFM** & **mIoU (\%)** & **FPS** \\ \hline Baseline & & & & 73.04 & 156.4 \\ DriveSegNet & ✔ & & & 73.65 & 149.3 \\ DriveSegNet & ✔ & ✔ & & 74.26 & 149.2 \\ DriveSegNet & ✔ & & ✔ & 73.86 & 142.1 \\ DriveSegNet & ✔ & ✔ & ✔ & 74.62 & 139.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation study of the proposed modules on the CamVid dataset. ✔ denotes the presence or inclusion of the component. As shown in Table 4, DriveSegNet-1 achieves a mIoU of 72.9% on the validation set with a processing speed of 102.5 FPS, while DriveSegNet-2 attains a mIoU of 77.3% with an inference speed of 70.5 FPS. This approach outperforms the previous BiSeNetFormer [33] model in both segmentation performance and processing efficiency.