Estimating Drone Visual Line-of-Sight Distance Using Machine Learning Approaches

In this study, we conducted flight tests to establish a clear standard for the visual line-of-sight (VLOS) distance of drones using machine learning models, as outlined in the Aviation Safety Act. Various machine learning models were applied and compared to predict the VLOS distance based on flight data. The analysis revealed that factors such as flight altitude, drone size, and observer’s vision significantly influence the VLOS distance. In particular, drone volume and observer’s vision were identified as the most important factors in predicting VLOS distance. The Random Forest Regression model demonstrated the best predictive performance, followed by the Polynomial Regression model. This study provides fundamental data to ensure safe drone operations and compliance with aviation regulations. The findings can also serve as practical resources for drone operators in planning safe flights. Future works should focus on collecting data from diverse environmental conditions to improve the generalization of prediction models. Additional research is also needed on beyond visual line-of-sight (BVLOS) and night flights, as these are critical areas for drone commercialization and require new predictive models and technological advancements to ensure safety.