## 1 Introduction  
Among the latest and most important milestones in transportation, autonomous driving systems aim to make road transport safer, optimize traffic, and make mobility generally more efficient. The enabling factors include recent developments in artificial intelligence, sensor technology, and real-time computing. An autonomous vehicle architecture at its very core has a multi-dimensional framework integrating perception, localization, planning, control, and connectivity. Perception refers to the capability of a vehicle to identify and interpret its environment through several sensors, including cameras, LiDAR, radar,and ultrasonic systems [1]. Localization techniques, using GPS and mapping, determine the precise position of the vehicle in its surroundings. Planning algorithms implement decisions on route optimization and obstacle avoidance, whereas control systems actuate these decisions through various mechanical operations performed by the vehicle [2]. Inside the extensive scope of autonomous driving, Image segmentation (IM) arises as an urgent innovation in the vehicle's control system [3]. The IM is a computer vision procedure that parts visual information into particular locales or portions, working with a more granular comprehension of the environment. This cycle is vital for semantic interpretation, as it permits the autonomous system to distinguish and separate features, for example, vehicles, walkers, traffic lights, and street symbols. Semantic segmentation allocates every pixel in a picture to a particular class, empowering a definite environmental understanding [4]. This capacity is especially basic for lane recognition and drivable region distinguishing proof, which are essential to guaranteeing safe and precise navigation. Image segmentation adds to real-time object location and classification, improving the vehicle's capability to keep away from deterrents and interact consistently with its environmental factors. By dividing and examining environmental conditions, the system can likewise adapt to changing environment and lighting conditions, for example, recognizing wet streets or distinguishing low-perceivability scenarios like snow and fog. This flexibility highlights the significance of robust and effective image segmentation algorithms, frequently controlled by profound learning models, for example, convolutional neural networks (CNNs). These models have fundamentally progressed the exactness and speed of the division process, making ongoing handling practical for autonomous driving applications [5]. Although it has an extraordinary potential, image segmentation in autonomous driving encounters a few difficulties. Guaranteeing performance in different environmental circumstances, like an extreme environment or occlusions, remains a vital area of concentration. Furthermore, continuous activity requires huge computational resources, presenting imperatives for on-board systems. Tending to these difficulties includes investigating enhanced neural architectures, for example, Transformer models, and incorporating multi-modular sensor data to improve precision and unwavering quality. Emerging patterns likewise highlight utilizing distributed cloud computing and edge innovations to handle hardware constraints and speed up processing abilities [6]. The earlier classical methods based on manual feature extraction faced difficulties with urban scenes due to occlusions, changing light conditions, and an abundance of diverse objects. While recent deep learning models have improved performance, problems remain [3]. In this case, this paper proposes a novel deep learning approach based on dynamically adaptable attention in GANs to address these limitations. This research integrates a new mechanism part that changes continuously to moving image complexities and a GAN-based architecture improved for multi-class segmentation tasks.