# A High-Quality Hybrid Mapping Model Based on Averaging Dense Sampling Parameters
## 1 Introduction  
Navigation maps [1, 2, 3], serving as a source of data, become more indispensable in fields such as autonomous driving [4], as more and more researchers use them for planning [5] and navigation decisions [6] in the context of autonomous vehicles. Point cloud data [7] and navigation systems [8] could acquire navigation maps, but they are time consuming, inefficient, and costly. In contrast, methods that use style transfer based on remote sensing images [9, 10, 11] to generate navigation maps are cost effective and offers real-time capability, which effectively address the aforementioned shortcomings. Style transfer [12, 13] is one of the most significant parts of navigation map generation, which transforms images from one domain to another by using a style image to convert a given image without altering its original content. It extracts features from the content and style of the image separately, adjusting and optimizing the weights of neural networks, and then fuses them to reconstruct the final image. Currently, navigation maps are mainly used in urban environments and in off-road settings. Especially in some scenarios like autonomous vehicle testing, the generation of navigation maps based on remote sensing images can provide valuable off-road image samples and accurate geographic information and real-time updates [14, 15, 16]. Nevertheless, style transfer faces challenges such as insufficient precision and high time cost. In order to overcome the obstacles, we propose an improvement in optimization methods, which fuses the parameters of generators sampled from different iterations in CycleGANs [17]. The weight average (WA) of deep learning models leverages the strengths of different models to compensate for individual model deficiencies, giving rise to improved predictive outcomes [18, 19]. However, the effect of CycleGAN combinedwith WA is often not ideal. Considering that WA based on Dense Stochastic Weighted averaging (SWAD) [20] increases the diversity of samples and introduces cycle consistency constraints, we also average the weights of generators with a similar dense sampling strategy. The method improves the overall quality of style transfer and generalizability in off-road environments, which provides users with the best route planning and traffic information. In addition, it can also improve traffic efficiency and develop urban planning and future transportation. Specifically, we improve the performance of CycleGAN by averaging the weights of generators sampled from different periods as shown in Figure 1. Image \(A\) is input into the network and transformed into _GeneratedB_ (image \(B\) ) by \(G_{A2B}\) (generators). Then, we establish a loss function between _Generated B_ and image \(B\) via \(D_{B}\) (discriminators). _Cyclic A_ is generated in reverse using \(G_{B2A}\) to establish the cycle loss, so as to maintain content consistency between _Generated B_ and \(A\). The SWAD is implemented within \(G_{A2B}\) and \(G_{B2A}\) by calculating weighted averages of model weights with the dense sampling strategy at different training stages. The method enables the model to efficiently extract features from off-road environments, improving its ability to generate navigation maps that blend both off-road and urban environments. Since each GAN model may have different training data and network architectures, the fusion process is able to mitigate the limitations of individual models and enhance the overall system's robustness, which makes it more adaptable to various input conditions [21; 22]. The contributions made by this paper are as follows: we propose a novel method that averages the parameters of generators sampled from different iterations with the dense sampling strategy in CycleGANs. We conduct a large number of experiments to compare the performance of the method on datasets from Google Maps [23]. Additionally, we conduct separate training for urban and off-road environments.