## 1 Introduction
These days, critical sensors for production-grade ADAS include sonar, radar and cameras. For long-range detection, ADAS typically utilizes radar, whereas nearby detection is performed through sonar. Recently, computer vision- and CNN-based systems can play a significant role in the pedestrian detection, lane detection and redundant object detection at moderate distances. The radar technology has performed significantly well for detecting vehicles with a deficiency in differentiating between different metal objects. This can register false positives on any metal objects, such as a small tin can. Moreover, sharp bends can make localization difficult for the radar technology. On the other hand, the sonar utility is compromised at high and slow speed, as well as it has a limited working distance of about two meters. In contrast to sonar and radar, cameras offer a richer set of features at lower costs. With a great deal of advancements in computer vision and deep learning, cameras could replace the other sensors for the driving assistance system. Video-based ADAS is intended to provide assistance to drivers in different road conditions [20]. In typical road scenes, most of the input pixels belong to large classes, such as buildings and roads, and thus, the network must generate a smooth segmentations. Designing a DNN engine that has the capability to delineate and segment moving and other objects based on their shapes despite their small sizes is needed. Therefore, it is significant to keep boundary details in the extracted image representation. From a computational perspective, it is essential for the network to be effective in terms of both computation time and memory during inference. The CSSA is aimed at offering an enhanced performance for the driving assistance system in different road and light conditions. By offering an improved boundary delineation and fast visual segmentation results, the proposed design could be an efficient choice for visual ADAS. The proposed system is trained and tested for the performance of road scene segmentation into 11 classes of interest for the road driving scenario. The key objective of this research is to propose a more enhanced and efficient CNN model. We have experimented with a number of activation functions (e.g., ReLU, Exponential Linear Units (ELU), PReLU, Maxout, Tanh), pooling methods (e.g., Avg, Max, stochastic), Batch Normalization (BN) methods and dropout units. We have also experimented with a reduced version of the proposed network. After a detailed analysis, the well-performing units are implemented in our final larger version of the proposed network, which is trained over road scene datasets. The proposed network architecture is stable, easy to manage and performs efficiently over all pervious benchmark results. We evaluate the performance of the CSSA on both CamVid and the Pascal VOC-12 datasets. The remainder of the paper is organized as follows. The first part of this paper presents the related work and an overview of the proposed architecture. The next section is about the system's working algorithm and architecture. Later, we present the experimentation and related training details. The last section is dedicated to the results and their analysis for the proposed system.