A Flight Conflict Detection Method for eVTOLs

Abstract

In the flight control process, ensuring the timely detection and effective resolution of flight conflicts is a core step in maintaining flight safety. We adopted a method that utilizes the binary classification function of the Support Vector Machine (SVM) technology to conduct deep learning on historical data of flight conflicts, thereby constructing an SVM model capable of accurately detecting flight conflicts. This model also incorporates the output results of the flight trend detection module, significantly enhancing the efficiency and accuracy of flight conflict detection. In practical operations, we carefully divided ADS-B (Broadcast Automatic Dependent Surveillance) data into training sets and validation sets. The training set was used to train the SVM model to enable classification capabilities; while the validation set was used to evaluate the classification performance of the SVM model. After multiple optimizations, we found the optimal parameters g and C for the SVM model, at which point the classification accuracy of the model reached 98%. This result strongly proves that by combining the flight trend detection module with the SVM and fully utilizing ADS-B information (including key data such as the longitude, latitude, altitude, speed, and heading of unmanned aircraft), we can achieve precise judgment of flight conflicts. This method not only improves the accuracy of flight conflict detection but also provides strong technical support for flight control work.