Explainable AI in Aerospace for Enhanced System Performance

Sutthithatip, Sujitra; Perinpanayagam, Suresh; Aslam, Sohaib; Wileman, Andrew

doi:10.1109/dasc52595.2021.9594488

Cited by 11 publications

(6 citation statements)

References 3 publications

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“…By employing a comprehensive XAI taxonomy and explanation framework, ref. [87] aims to enhance the decision-making processes in aerospace by integrating human-centric and scientific explanations. The related work discussed in this paper focuses on the implementation of XAI in aerospace, emphasizing its application in supporting operators such as pilots and air traffic controllers in making informed decisions based on meaningful information within time constraints.…”

Section: Aviationmentioning

confidence: 99%

Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications

Sharma,

Chand,

Buksh

et al. 2024

Algorithms

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This study delves into the realm of Explainable Artificial Intelligence (XAI) frameworks, aiming to empower researchers and practitioners with a deeper understanding of these tools. We establish a comprehensive knowledge base by classifying and analyzing prominent XAI solutions based on key attributes like explanation type, model dependence, and use cases. This resource equips users to navigate the diverse XAI landscape and select the most suitable framework for their specific needs. Furthermore, the study proposes a novel framework called XAIE (eXplainable AI Evaluator) for informed decision-making in XAI adoption. This framework empowers users to assess different XAI options based on their application context objectively. This will lead to more responsible AI development by fostering transparency and trust. Finally, the research identifies the limitations and challenges associated with the existing XAI frameworks, paving the way for future advancements. By highlighting these areas, the study guides researchers and developers in enhancing the capabilities of Explainable AI.

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Section: Aviationmentioning

confidence: 99%

Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications

Sharma,

Chand,

Buksh

et al. 2024

Algorithms

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“…AI technology is also being applied to spacecraft manufacturing and operation. AI algorithms are being applied to advanced guidance and control systems of aerospace [69], and explainable AI technologies that help pilots make decisions, are being studied [70]. In a similar context, research and surveys related to autonomous spacecraft operation are also being actively conducted [69,71].…”

Section: Trends Of Ai Technology Used In Spacementioning

confidence: 99%

Review for Examining the Oxidation Process of the Moon Using Generative Adversarial Networks: Focusing on Landscape of Moon

2022

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Japan Aerospace Exploration Agency (JAXA) has collected and studied the data observed by the lunar probe, SELenological and ENgineering Explorer (SELENE), from 2007 to 2017. JAXA discovered that the oxygen of the upper atmosphere of the Earth is transported to the moon by the tail of the magnetic field. However, this research is still in progress, and more data are needed to clarify the oxidation process. Therefore, this paper supplements the insufficient observation data by using Generative Adversarial Networks (GAN) and proposes a review paper focusing on the methodology, enhancing the level of completion of the preceding research, and the trend of examining the oxidation process and landscape of the moon. We propose using Anokhin’s Conditionally-Independent Pixel Synthesis (CIPS) as a model to be used in future experiments as a result of the review. CIPS can generate pixels independently for each color value, and since it uses a Multi-Layer Perceptron (MLP) network rather than spatial convolutions, there is a significant advantage in scalability. It is concluded that the proposed methodology will save time and costs of the existing research in progress and will help reveal the causal relationship more clearly.

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“…To overcome these obstacles, several researchers are focusing on solving these problems with the final purpose of finally enabling AI certifiability. In this line, [24] is focused on uncovering complex AI models to human users in a highly systematic, interpretable, and understandable manner, named as Explainable AI (XAI). [25] identifies the challenges and techniques to address the issues posed by learning-enabled components.…”

Section: • Requirements Traceability and Coveragementioning

confidence: 99%

“…solve the traceability and non-deterministic problems, easing their future homologation. Several works including [24] [30] [35] present recent developments and applications in this field.…”

Section: Thus Paying Attention To Interpretable Ai When Designing a N...mentioning

confidence: 99%

Machine Learning Based Visual Navigation System Architecture for Aam Operations with A Discussion on its Certifiability

Escudero

Costas

Hardt

et al. 2022

2022 Integrated Communication, Navigation and Surveillance Conference (ICNS)

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Advanced Air Mobility (AAM) is expected to revolutionize the future of general transportation expanding the conventional notion of air traffic to include several services carried out by autonomous aerial platforms. However, the significant challenges associated with such complex scenarios require the introduction of sophisticated technologies able to deliver the resilience, robustness, and accuracy needed to achieve safe, autonomous operations [39]. In this context, solutions based on Artificial Intelligence (AI), able to overcome some limitations found in traditional approaches, are becoming a major opportunity for the aviation industry, but, at the same time, a significant challenge with respect to the certification standards.With the focal point on further proposing a certifiable architecture for AI-enhanced vision navigation in AAM operations, this paper first, summarizes the current technologies and fusion methods applied to date to navigation purposes, to later address the certification problem. Regarding certification, it explores three specific points: 1) traditional certification procedures; 2) current status of AI homologation recommendations; and 3) other certification factors to be considered for future discussion.

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Explainable AI in Aerospace for Enhanced System Performance

Cited by 11 publications

References 3 publications

Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications

Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications

Review for Examining the Oxidation Process of the Moon Using Generative Adversarial Networks: Focusing on Landscape of Moon

Machine Learning Based Visual Navigation System Architecture for Aam Operations with A Discussion on its Certifiability

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