Electronics Optics & Control, Volume. 32, Issue 6, 99(2025)
Design and Functional Hazard Assessment of Visual Landing System for Runway Detection
[1] [1] Daedalean. Daedalean concluded a joint research project with the FAA on Neural Network-Based Runway Landing Guidance for General Aviation[EB/OL]. (2022-05-24) [2024-04-19]. https://daedalean.ai/tpost/g2s3nhz4u1-daedalean-concluded-a-joint-research-pro.
[2] [2] Acubed. Airbus validates computer vision-based technologies to increase safety through automation[EB/OL]. (2023-01-12)[2024-04-19]. https://acubed.airbus.com/blog/wayfinder/airbus-validates-computer-vision-based-technologies-to-increase-safety-through-automation.
[3] [3] Xwing. Journey to autonomy: the power of Xwing air data for deep learning[EB/OL]. (2023-10-21)[2024-04-19]. https://www.xwing.com/post/journey-to-autonomy-the-power-of-xwing-air-data-for-deep-learning.
[5] [5] CHEN M Q, HU Y Z. An image-based runway detection method for fixed-wing aircraft based on deep neural network[J]. IET Image Processing, 2024, 18(8): 1939-1949.
[8] [8] MA N, WENG X R, CAO Y F, et al. Monocular-vision-based precise runway detection applied to state estimation for carrier-based UAV landing[J]. Sensors, 2022, 22(21): 8385.
[9] [9] MAMALET F, JENN E, FLANDIN G, et al. White paper machine learning in certified systems[R]. Toulouse: IRT Saint Exupry, 2021.
[10] [10] SAE. Artificial intelligence in aeronautical systems: statement of concerns: AIR6988[S]. Pittsburgh: Society of Automotive Engineers, 2021.
[12] [12] BALDUZZI G, FERRARI BRAVO M, CHERNOVA A, et al. Neural network based runway landing guidance for general aviation autoland[R]. Atlantic City: Federal Aviation Administration. William J. Hughes Technical Center, 2021.
[13] [13] CLUZEAU J M, HENRIQUEL X, REBENDER G, et al. Concepts of design assurance for neural networks (CoDANN)[R]. Cologne: European Union Aviation Safety Agency, 2020.
[14] [14] DURAND J G, DUBOIS A, MOSS R J. Formal and practical elements for the certification of machine learning systems[C]//IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC). Barcelona: IEEE, 2023: 1-10.
[15] [15] SOUDAIN G. EASA concept paper: guidance for Level 1 & 2 machine learning applications[R]. Cologne: European Union Aviation Safety Agency, 2023.
[16] [16] DUCOFFE M, CARRERE M, FLIERS L, et al. LARD-landing approach runway detection-dataset for vision based landing[EB/OL]. [2024-04-30]. https://ar5iv.labs.arxiv.org/html/2304.09938.
[18] [18] ISO. Safety of the intended functionality: ISO/PAS 21448[S]. Geneva: International Organization for Standardization, 2022.
[21] [21] SCHWEIGER A, ANNIGHOEFER B, REICH M, et al. Classification for avionics capabilities enabled by artificial intelligence[C]//IEEE/AIAA 40th Digital Avionics Systems Conference (DASC). San Antonio: IEEE, 2021: 1-10.
[23] [23] DEY S, LEE S W. Multilayered review of safety approaches for machine learning-based systems in the days of AI[J]. Journal of Systems and Software, 2021, 176: 110941.
[24] [24] MOSS R J, KOCHENDERFER M J, GARIEL M, et al. Bayesian safety validation for black-box systems[C]//AIAA AVIATION 2023 Forum. San Diego: AIAA, 2023: 3596.
[25] [25] DONG Y, HUANG W, BHARTI V, et al. Reliability assessment and safety arguments for machine learning components in system assurance[J]. ACM Transactions on Embedded Computing Systems, 2023, 22(3): 48.
[27] [27] DION B, NAJORK M, DALMASSO N, et al. Programming neural networks inference in a safety-critical simulation-based framework[C]//The 11th European Congress on Embedded Real Time Software and Systems (ERTS). Toulouse: SEE and 3AF associations, 2022: 1-12.
[28] [28] DMITRIEV K, RHEIN J, BELLER L, et al. Safety assessment of a machine learning-based aircraft emergency braking system: a case study[C]//2024 AIAA DATC/IEEE 43rd Digital Avionics Systems Conference (DASC). San Diego: IEEE, 2024: 1-10.
Get Citation
Copy Citation Text
DONG Lei, LIU Jiachen, CHEN Xi, SHI Xinru, WANG Peng. Design and Functional Hazard Assessment of Visual Landing System for Runway Detection[J]. Electronics Optics & Control, 2025, 32(6): 99
Category:
Received: Apr. 30, 2024
Accepted: Jun. 12, 2025
Published Online: Jun. 12, 2025
The Author Email: