## 1 Introduction
For instance, the enhancement of segmentation accuracy in numerous networks is predicated on the adoption of complex architectures; however, such strategies augment computational complexity, consequently impeding inference speed, notably in real-time applications, including autonomous driving and medical imaging. Furthermore, even with the incorporation of multi-scale fusion techniques, certain methodologies still manifest limitations in their capacity to comprehensively capture global contextual information, thereby constraining boundary precision and detail retention. To mitigate the inherent compromise of spatial detail and contextual information characteristic of current semantic segmentation networks, while simultaneously enhancing computational efficiency, Dynamic Real-time Integrated Vision Encoder-Segmenter Network (DriveSegNet) is introduced. This framework encompasses several key innovations:  
1. Positional Convolutional Attention Module (PCAM): Conventional dilated convolutions and uniform pooling strategies exhibit limitations in their ability to capture multi-scale contextual information. PCAM addresses this by integrating convolutional operations with self-attention mechanisms, facilitating the adaptive extraction of semantic cues across multiple scales and enhancing the discriminability of feature representations. This approach enables the construction of more effective global inter-dependencies among features, in contrast to traditional fixed-receptive-field methods, thus improving the model's capacity to process intricate scenes. 2. Lightweight Adaptive Fusion Module (LAFM): Feature fusion in existing methodologies typically relies on simple concatenation or element-wise addition, approaches that inadequately address spatial and semantic relationships between features. LAFM, conversely, dynamically adjusts feature contributions based on spatial and semantic importance. This module employs a lightweight configuration, combining global average pooling with convolution operations to generate spatially adaptive attention weights, guiding feature fusion and emphasizing semantically significant regions while curtailing computational overhead. 3. To enhance feature flow and information propagation within deep neural networks, novel skip connections are introduced. Departing from traditional methods that utilize mere addition, our approach integrates convolved feature maps with original features at multiple stages. This preserves low-level information in earlier stages and concurrently facilitates enhanced learning of high-level features. These refined skip connections further promote the efficacious integration of multi-scale features, yielding a more robust architecture. 4. Building upon the aforementioned modules, DriveSegNet is presented as a lightweight yet effective network tailored for real-time semantic segmentation. Through the strategic integration of PCAM and LAFM within an encoder-decoder architecture, coupled with the employment of enhanced skip connections, DriveSegNet attains a favorable equilibrium between segmentation accuracy and computational efficiency. Extensive experimentation on the Cityscapes dataset demonstrates that DriveSegNet outperforms the baseline model by 2.36% in mean Intersection over Union (mIoU), while maintaining high inference speed, rendering it suitable for practical real-time applications.