## 1 Introduction
Their survey [12] refers to 37 articles devoted to assuring the system's reliability and fault tolerance. Data from sensors can be processed by computer vision algorithms to solve navigation and monitoring problems, as presented in [13]. The practical applications of autonomous robots may be diverse. Such robots are especially useful when the manual operator's control is associated with significant risks to their health; while our research studies the employment of AVs in Arctic region extreme conditions, other authors, for example, consider AV operation in underground mines [14] and in radioactive conditions [15]. The problem of designing a reliable navigation system is related to the task of ensuring the safety of the vehicle. [PERSON] et al. [12] deliberated on AV navigation security issues related to the system's reliability and fault tolerance. The task of controlling an autonomous vehicle was considered by [PERSON] et al. [16]. The technology was focused on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in connected and automated vehicles at the macroscopic level. The task of developing a navigation system for electric vehicles is more specific. Various scenarios for driving electric vehicles were considered by [PERSON] et al. [17]. The results presented in our work also contribute to improving the safety of using AVs in various applications, e.g., autonomous transport navigation in extreme weather conditions. The issue of sensor fusion is well covered in the literature. For example, [PERSON] et al. [18] discussed AV kinematic and dynamic synthesis for sideslip angle estimation based on a consensus Kalman filter. [PERSON] et al. [PERSON] et al. (2019) studied slip angle estimation by considering a signal measurement characteristic. In our work, we investigated methods of improving navigation performance in extreme environmental and poor GPS signal communication conditions. We designed and developed a complimentary AV computer-vision-based localization system to make it robust in harsh environmental conditions that cause the degradation of images obtained from the on-board camera. This computer vision recognition system should integrate with the conventional GPS or even replace it when it becomes unavailable. In particular, we investigated and verified our design on an Arctic region use case, as, nowadays, many authorities seek to automate their transportation operations in this region, and AVs are deemed to become a prominent solution in this field [PERSON] et al. (2019). An example of the resource excavation facilities cluster in the Arctic region is demonstrated in Figure 1. Highly limited natural visual landmarks and changing weather conditions in this region make navigation a real challenge for the AVs. In our particular use case, we considered specialized artificial visual tags allocated over the AV operation area. These tags contain GPS coordinates encoded via AprilTag (2018), which is needed to be detected and properly recognized by AVs in order to perform localization in case of GPS unavailability. To implement our design, we employed Nvidia Jetson AGX Xavier as the computing device for the developed ML-based system verification. To enhance our ML-based model robustness toward DQ degradation, we generated images of the artificial visual tags affected by the various lightning and harsh weather conditions, such as blizzards and heavy rain. In addition, we employed several types of affine transformations to these images to diversify our training set. We utilized the produced set of images to re-train our ML-based image classification model. Then, we evaluated the performance of the developed ML-based model to detect and recognize the artificial visual tags from the varied distances. This paper's major contribution is our computer-vision-based localization system design that might be employed by AVs in order to improve their localization and mapping accuracy in case of GPS signal unavailability.