Computer Vision based Autonomous Taxiing of Aircraft

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This paper proposes a computer vision based autonomous system for the taxiing of an aircraft in the real world. The system integrates both lane detection and collision detection and avoidance models. The lane detection component employs a segmentation model consisting of two parallel architectures.  An airport dataset is proposed, and the collision detection model is evaluated with it to avoid collision with any ground vehicle. The lane detection model identifies the aircraft's path and transmits control signals to the steer-control algorithm. The steer-control algorithm, in turn, utilizes a controller to guide the aircraft along the central line with 0.013 cm resolution. To determine the most effective controller, a comparative analysis is conducted, ultimately highlighting the Linear Quadratic Regulator (LQR) as the superior choice, boasting an average deviation of 0.26 cm from the central line. In parallel, the collision 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 modules achieve true positive rates of 92.59% and 85.19%, respectively.