In this paper, we present a novel autonomous vehicle (AV) localization design and its implementation, which we recommend to employ in challenging navigation conditions with a poor quality of the satellite navigation system signals and computer vision images. In the case when the GPS signal becomes unstable, other auxiliary navigation systems, such as computer-vision-based positioning, are employed for more accurate localization and mapping. However, the quality of data obtained from AV’s sensors might be deteriorated by the extreme environmental conditions too, which infinitely leads to the decrease in navigation performance. To verify our computer-vision-based localization system design, we considered the Arctic region use case, which poses additional challenges for the AV’s navigation and might employ artificial visual landmarks for improving the localization quality, which we used for the computer vision training. We further enhanced our data by applying affine transformations to increase its diversity. We selected YOLOv4 image detection architecture for our system design, as it demonstrated the highest performance in our experiments. For the computational platform, we employed a Nvidia Jetson AGX Xavier device, as it is well known and widely used in robotic and AV computer vision, as well as deep learning applications. Our empirical study showed that the proposed computer vision system that was further trained on the dataset enhanced by affine transformations became robust regarding image quality degradation caused by extreme environmental conditions. It was effectively able to detect and recognize images of artificial visual landmarks captured in the extreme Arctic region’s conditions. The developed system can be integrated into vehicle navigation facilities to improve their effectiveness and efficiency and to prevent possible navigation performance deterioration.

The aim of this study is to explore and validate the potential of using brain-computer interfaces (BCIs) and electroencephalography (EEG) signals to control external devices, specifically unmanned aerial vehicles (UAVs). The main problem of research is to integrate between human cognitive functions with Internet of things (IoT) devices, enabling hands-free control based on mental commands. Such an approach could transform fields where rapid, precise, and intuitive control is critical, including healthcare, search-and-rescue missions, and industrial automation. By capturing raw EEG data associated with simple motor commands, we demonstrate the feasibility of translating specific brainwave patterns into UAV instructions like "hover" and "land." The next phase will involve analog-to-digital processing and filtering to refine these signals, preparing them for machine learning algorithms to enhance command accuracy. Our findings highlight the promise of neural interfaces as a bridge between human thought and machine action, paving the way for advancements in IoT-based human-technology interaction.

Autonomous vehicles (AV) applications can extend to reach variable fields, some fields are more impacted with this interference than others, farming, wildfire detecting, search and rescue, weed control, and livestock management are critical and can be enhanced in large range using the integration between artificial intelligence (AI) and AV’s. Thus, in this research, we try to describe a process of making a singular multitask AV with sufficient processing power, and an accurate AI functionality to perform such tasks in real time. In order to achieve such target, multiple requirements are essential such as an accurate flight controller, a high-end processing unite, and aerodynamical design to operate in severe weather conditions, as well as precise and high-quality materials for 3D-printing and execution.