## 7 Conclusions  
AV navigation is a challenging problem in the conditions of GNSS signal instability or absence, which might often be the case in certain regions as diverse as dense architecture urban areas or low populated areas with extreme environmental conditions. To address this problem, data from other AV sensor systems are commonly employed to maintain the required localization and mapping accuracy. However, the quality of measurements produced by those sensors might be affected by various factors; for example, the quality of images from the on-board camera might be degraded by harsh weather conditions. This leads to the AV's navigation performance deterioration, decreases the AV's effectiveness and efficiency, and might jeopardize the safety of AVs and surrounding objects. In this paper, we proposed an enhanced design of a computer-vision-based localization system that can be integrated into AV navigation facilities to improve their localization and mapping accuracy in extreme environmental conditions where GPS signals are unstable or absent. Based on our empirical study results, the following conclusions can be formulated. A CNN-based ML model architecture (in particular, YOLOv4), demonstrated performance that was higher than the two other investigated architectures and can be employed as an image processor for AV localization applications. However, as our research showed, even the model trained on a comprehensive set of images representing affects by various extreme environmental conditions, was insufficient to achieve performance acceptable for effective and efficient AV operation. Employing data augmentation techniques such as affine transformations and further training the model on this data significantly improved the model robustness regarding testing image quality degradation. Indeed, the further trained model was able to demonstrate a performance higher than 80% for the degraded images captured from the distance of up to 20 m. In the changing illumination conditions, the model was able to achieve high performance in the distance of up to 2 m in daylight. Unfortunately, in the nighttime, when the distance increased above 0.5 m, the model was hardly able to properly recognize the tag.  
**Author Contributions:** Conceptualization, L.R. and I.V.; methodology, S.C.; software, P.B.; validation, P.B.; formal analysis, R.G.; investigation, P.B.; resources, E.N.; data curation, P.B. and E.N.; writing--original draft preparation, S.C.; writing--review and editing, R.G. and L.R.; visualization, S.C.; project administration, L.R., R.G., E.N. and I.V.; funding acquisition, L.R. and I.V. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The work of [PERSON] and [PERSON] was supported by the United States Military Academy (USMA), Grant Number W911 NF-20-1-0337. The work of [PERSON], [PERSON], [PERSON], and [PERSON] was supported by the Ministry of Science and Higher Education of the Russian Federation, known as \"Goszadanie\" \(N\)075-01024-21-02 of 29.09.2021 (project FSEE-2021-0014).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study is available on request from the authors: I.V. and P.B. The data is not publicly available due to it is a commercial company's intellectual property.  
**Conflicts of Interest:** The authors declare no conflict of interest.