## 3 Integration of the Computer-Vision-Based Localization into the AV's Navigation Stack  
The generic AV navigation process can be described as follows: if GPS is available, it is employed as the primary AV navigation system to perform route planning and proceeding to the target destination. By default, computer-vision-based localization is considered as the auxiliary navigation system (ANS). Depending on the AV's composition, it might also employ other ANSs, such as IMU or radar. ANSs might be used in combination with GPS or with each other (which is a typical practice) or individually if one or more navigation components fail [30]. ANSs are routinely used to correct the GPS navigation error and for precise positioning and obstacles detection. Based on the available data from the navigation and localization system components, the AV makes decisions on further driving actions. Figure 2 schematically represents the routine process of decision making depending on the navigation and localization components' availability and their performance. For the sake of demonstration, an IMU was used here as another ANS that the AV was equipped with. Initially, the availability of the GPS signal was verified. With a successful response from the GPS, the system state switches to the IMU availability verification. The main problem with relying solely on the IMU for the navigation and localization operations is the complex environmental conditions in which the AV has to operate. When primarily relying on the IMU, the navigation error is rapidly accumulated [31], which ultimately leads to the AV's disorientation. In this case, such an error can be corrected by the GPS coordinates; however, the GPS system might be unavailable for a considerable time. In this case, localization and further navigation might be performed based on computer vision in combination with other available ANSs. The problem of time synchronization for a IMU/GNSS is also challenging for reliable navigation, and it is addressed in the following way: the AV's sensors operate in series, not in parallel. In the sequential operation of sensors, the data is transmitted from one system to another, thus receiving additional parameters for work. The information first passes through the IMU, then through the GNSS. The use of AprilTags makes it possible to verify inaccuracies in the IMU and correct their impact on the navigation performance. The computer-vision-based localization system should be capable of detecting and retrieving the information from the artificial visual landmark in the current location. The architecture of the proposed computer-vision-based localization incorporates the following basic modules:  
* A detection module;
* An approach module;
* A retrieval module. The architecture of these modules and the connections between them are represented in Figure 3. The detection module operates based on the implemented and trained ML model, the approach module is implemented based on the Nvidia Isaac \"Followme\" module [32], and the retrieval module utilizes the AprilTag recognition function [21]. Each module performs unique functions within the overall AV navigation system. The detection module is responsible for detecting the artificial visual landmark on which the AprilTag with the visually encoded GPS coordinates is printed. The approach module allows for pointing and holding the detected landmark in the field of the AV's on-board camera view while the AV is traveling closer to properly recognize the tag. The retrieval module is responsible for retrieving the information on the current GPS coordinates from the visual tag, and it performs the AV's localization adjustment according to these coordinates. In the case of two or more landmarks appearing in the on-board camera's field of view simultaneously, the one that is detected with a higher performance is selected for proceeding toward it.