## 1 Introduction  
Navigation is one of the most important autonomous vehicle (AV) functions that performs positioning in the environment and decides on further mobility actions. The failure of accurate localization decreases AV effectiveness and efficiency in other operations, and this might result in safety accidents. Contemporary AVs primarily rely on global navigation satellite systems (GNSSs) to perform navigation and localization, such as GPS or GLONASS, which provide a global coverage and a high accuracy position. For example, contemporary GPS-based methods, which are the most commonly used GNSS systems, are capable of achieving a subcentimetric accuracy (e.g., RTK DGPS [1]). This level of accuracy, which could be further complemented by the measurements from other AV's on-board sensors, is sufficient for AVs to safely and efficiently navigate their surroundings. However, in some territories, e.g., dense urban or low populated areas with frequent extreme environmental conditions such as the Arctic region, the GPS signal can become unstable or even absent,which exacerbates navigational operations. In addition to communication obstructions and environmental conditions, other factors can lead to GPS failures, such as electronic interference that may be caused by a variety of sources, e.g., other electronic devices or power lines; signal jamming that can be intentionally activated by various parties or malicious attackers; and software and hardware malfunctions in GPS-related components that can cause failures. To mitigate these factors, multi-sensor perception may be employed for localization and mapping improvement. The data from multi-modality sensors, such as an on-board camera and an inertial measurement units (IMUs), might be fused in order to determine the AV's position in relation to the surrounding objects. However, these sensor devices might also fail or be maliciously attacked, which leads to data quality (DQ) degradation and jeopardizes the AV's security and safety [2;3]. Obviously, the localization accuracy in this case depends on the quality of data acquired from the sensors. The data from multiple sensors might be fused to enhance its accuracy and quality; however, the sensors need to be properly selected in this case [4]. This operation might employ various sensor selection techniques, such as genetic-algorithms-based ones [5]. In this paper, we consider computer-vision-based AV localization as a complimentary system to perform navigational operations. Since computer vision utilizes intelligent machine learning (ML) techniques, its operation involves initial procedures of the ML model training and validating. In most cases, such procedures commonly employ only high quality training data [6], which allows ML-based systems to achieve reliable performance on high quality testing images. However, our previous investigations demonstrated that using real life images with the degraded quality images dramatically affects the performance of ML-based computer vision classification [7;8]. To prevent ML performance degradation, multiple techniques might be leveraged, including the increase of training set diversity and the re-training of ML-based systems on samples of varying DQ [9]. The AV's navigation operation largely depends on how the sensor system is designed. One of the well-known sensors used for AV navigation is LiDAR. For example, [PERSON] et al. [10] considered a navigation and localization system consisting of LiDARs and on-board cameras. The system performed path segmentation from point cloud data, which could be further used in computer vision and AV localization. AVs, as well as unmanned aerial vehicles, can rely on their own sensors to analyze the environment. The navigation and localization system allows AVs to make decisions about further actions without the operator. [PERSON] et al. [11] discussed ways to improve the navigation system for the Internet of Drones using ML and deep learning algorithms.