Developing a computer vision based system for autonomous taxiing of aircraft

Authors of this paper propose 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 archi...

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Veröffentlicht in:Aviation (Vilnius, Lithuania) Jg. 27; H. 4; S. 248 - 258
Hauptverfasser: Gaikwad, Prashant, Mukhopadhyay, Abhishek, Muraleedharan, Anujith, Mitra, Mukund, Biswas, Pradipta
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Vilnius Vilnius Gediminas Technical University 29.12.2023
Schlagworte:
Aircraft
Aircraft detection
airport dataset
Algorithms
autonomous taxi
Collision avoidance
Computer vision
Control algorithms
Control theory
Controllers
Datasets
lane detection
lane navigation
Linear quadratic regulator
object detection
Taxiing
Vision systems
lane navigation
collision avoidance
object detection
airport dataset
lane detection
autonomous taxi
ISSN:1648-7788, 1822-4180
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Zusammenfassung:Authors of this paper propose 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. First published online 4 January 2024
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1648-7788
1822-4180
DOI:10.3846/aviation.2023.20588