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Autonomous Taxiing of Aircraft using Machine Vision Approach

This work presents a vision-guided, novel, and robust system for the autonomous taxiing of an aircraft in the real world. The system is an ensemble of autonomous navigation and collision avoidance modules. The navigation module detects the lane and sends the control signal to the steer-control algorithm. This algorithm uses a controller to help the aircraft follow the central line with a resolution of 0.013 cm. An airport dataset is proposed, and the object detection model is evaluated with it to avoid collision with any ground vehicle. In parallel, the object detection model is also compared with other state-of-the-art models on the same dataset and proved its superiority. A detailed study is conducted in different lighting conditions to prove the efficacy of the proposed system. It is observed that lane detection and collision avoidance module work with a true positive rate of 92.59% and 85.19%, respectively.