Application of Improved Q-Learning Algorithm in Dynamic Path Planning for Aircraft at Airports

Xiang, Zheng; Sun, Heyang; Zhang, Jiahao

doi:10.1109/access.2023.3321196

Cited by 5 publications

(1 citation statement)

References 36 publications

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“…Note that, in some cases, both decision support and autonomous applications can be developed for the same area. For instance, in autonomous taxiing, RL may be used to predict taxiing time [17] or to direct the path of the aircraft [18].…”

Section: Applications Of Reinforcement Learning In Aviationmentioning

confidence: 99%

Certification of Reinforcement Learning Applications for Air Transport Operations Based on Criticality and Autonomy

Ribeiro,

Tseremoglou,

Santos

2024

AIAA SCITECH 2024 Forum

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Despite its success in various research domains, Reinforcement Learning (RL) faces challenges in its application to air transport operations due to the rigorous certification standards of the aviation industry. The existing regulatory framework fails to provide adequate, acceptable means of compliance for RL applications, and thus, there is no legal framework for their safe deployment yet. Guidelines must be formulated to certify RL models aimed at air transport operations to enable real-world utilisation of these promising methods. These guidelines must consider the unique characteristics of these models, deviating from the methodology of current guidelines crafted before the emergence of ML applications. The paper proposes novel certification requirements for RL models based on their technical characteristics, safety-criticality, and autonomy. This framework covers the choice of the RL algorithm and analyses the actions, agents, environment, and potential hazards and risks of the RL application. Additionally, this work outlines the evidence the certification applicant must present to demonstrate compliance with these requirements. While this framework is not a complete solution for the complex problem of certifying RL, it is intended to serve as an initial framework which can be extended in cooperation with regulatory entities.

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Section: Applications Of Reinforcement Learning In Aviationmentioning

confidence: 99%

Certification of Reinforcement Learning Applications for Air Transport Operations Based on Criticality and Autonomy

Ribeiro,

Tseremoglou,

Santos

2024

AIAA SCITECH 2024 Forum

View full text Add to dashboard Cite

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Increasing customer service competencies of airports: virtual integration competence, warmth and intimacy of artificial intelligence services

2024

BPMJ

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PurposeAirports are an essential part of the airline value chains. Artificial intelligence (AI) technologies are widely used at airports; the study aims to explore how the virtual integration competence and the perceived warmth of AI in airports increase customer service competencies and satisfy their passengers.Design/methodology/approachBased on the perspectives of digital competencies and hybrid intelligence, a continued usage intention model was analyzed using the partial least squares approach; this study used purposed sampling to collect data from those airports; participants who adopted the AI service in airports in Beijing, Taipei and Singapore who have the potential to use AI service usage experience more than three times and 384 completed questionnaires were analyzed.FindingsAI innovations serve human tasks at airports and analytics applications as change drivers and can replace legacy procedures. The research findings help point out the perceived warmth of AI and the virtual integration competence of airports utilizing the intimacy of AI services.Originality/valueAI innovations provide a service change to replace human tasks and intelligence and analytics applications at airports. AI services are a powerful tool for airports to serve their passengers efficiently; airports will collaborate with airlines to offer AI services to passengers.

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A Review of Reinforcement Learning for Fixed-Wing Aircraft Control Tasks

Richter,

Calix,

Kim

2024

IEEE Access

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Application of Improved Q-Learning Algorithm in Dynamic Path Planning for Aircraft at Airports

Abstract: This paragraph of the first footnote will contain support information, including sponsor and financial support acknowledgment.

Cited by 5 publications

References 36 publications

Certification of Reinforcement Learning Applications for Air Transport Operations Based on Criticality and Autonomy

Certification of Reinforcement Learning Applications for Air Transport Operations Based on Criticality and Autonomy

Increasing customer service competencies of airports: virtual integration competence, warmth and intimacy of artificial intelligence services

A Review of Reinforcement Learning for Fixed-Wing Aircraft Control Tasks

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