Human-Machine Interaction Efficiency Factors in Flight Simulator Training Towards Chinese Pilots

Li, Qiang; Li, Binbin; Wang, Nan; Li, Wenxi; Lyu, Zhengfang; Zhu, Yancong; Liu, Wei

doi:10.1007/978-3-030-51064-0_4

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…Shmelova et al [13] have proposed to use ML and text analysis to train the air traffic controllers to decrease human factor-related accident rates. Li et al [14] have explored the factors in flight simulators that could improve the human-machine interaction efficiency. They have adopted metacognitive strategy to help pilots to coordinate cognitive resources and gradually form flight experience based on their characteristics.…”

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence in Pilot Training and Education – Towards a Machine Learning Aided Instructor Assistant for Flight Simulators

Yang

Lammers

et al. 2021

Communications in Computer and Information Science

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The a viation industry was set to see unprecedented growth over the next two decades. Key occupations predicted to be in shortage included not only pilots, but also flight instructors. Undoubtedly, Covid-19 is currently having a huge impact on the industry. Nevertheless, the current environment further strengthens the need for pilots to maintain their training. Consequently, there is pressure to deliver high-quality training outcomes for an increasing number of pilots and trainees with limited resources available. Current simulator-based training schemes are limited by placing a significant reliance on the personal experience of flight instructors to assess pilot performance. Finding ways to increase the quality and efficiency of simulator-based training is therefore of high importance. With recent advances in artificial intelligence, it is possible to use machine learning techniques to extract latent patterns from massive datasets, to analyze pilot trainees' activities, and to provide feedback on their performance by processing hundreds of different parameters available on flight simulators. An ML-aided pilot training and education framework is needed that exploits the power of the ML techniques for more objective performance evaluation. In this paper, we describe a conceptual framework for such a system and outline steps toward the development of a full digital instructor system with the potential to overcome current limitations and enabling comprehensive and meaningful feedback that is tailored to the individual need of the trainee.

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Section: Related Workmentioning

confidence: 99%

Artificial Intelligence in Pilot Training and Education – Towards a Machine Learning Aided Instructor Assistant for Flight Simulators

Yang

Lammers

et al. 2021

Communications in Computer and Information Science

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“…Their research advocates for prioritizing flight instructors with extensive total flight time during recruitment. Li, Qiang et al [7] delved into factors influencing flight training efficiency for Chinese pilots. They identified pilot stress, training interaction, and the application of metacognitive strategies as paramount.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Training Cost and Difficulty for Aircraft-Type Transition Based on Similarity Assessment

Cao,

Zhang,

Feng

2024

Aerospace

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As aviation technology advances, numerous new aircraft enter the market. These not only offer airlines technological and fuel efficiency advantages but also present the challenge of how to conduct pilots’ aircraft-type transition training efficiently and economically. To address this issue, this study designed a methodology to quantitatively assess the similarity in panel display control design and standard operating procedures (SOPs) between aircraft types. Then, by combining the results of a questionnaire survey on A320, A330, B737, and B777 transition training and training cost data, it was verified quantitatively that inter-aircraft similarity has a positive impact on reducing the difficulty and cost of transition training. Taking the similarity in aircraft types as a feature, the KNN algorithm was used to successfully construct a difficulty prediction model for the training program of aircraft-type transition training. To overcome the limitation of insufficient training cost data volume, this study adopts the transfer learning method to construct a prediction model of the transition training cost, and the final significant prediction accuracy proves the effectiveness of the method. The research in this paper not only provides strong data support for the resource planning and cost management of airlines’ aircraft-type transition training but also provides new research perspectives and methodological guidance for the field of aviation training.

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“…Elmahjub E et al [14] discussed the transformative extraction of key features from videos and images obtained by computer vision algorithms, as well as improvements in traffic flow analysis methods, risk assessment, accident investigation, and road damage assessment. However, the obstacles faced by the large-scale deployment of unmanned vehicle cooperative operation technology are complex and interrelated, and strategies to overcome these obstacles and their impacts also need to be addressed [15][16][17] . In summary, while there is extensive research on vehicle and aircraft conflict detection and risk assessment methods, studies considering the mixed operation of unmanned vehicles and humanpiloted aircraft within airport environments are scarce.…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo Simulation-Based Risk Assessment for Unmanned Ground Equipment Taxiing Guidance

2024

jeis

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Due to the potential safety hazards such as incorrect or missed aircraft guidance caused by human-operated guiding, the introduction of unmanned guiding vehicles can effectively reduce these unsafe events. However, the risks associated with unmanned driving guiding vehicles in the process of guiding aircraft taxiing have not yet been thoroughly and quantitatively studied. This paper collects the kinematic parameters of the unmanned driving guiding vehicle during the process of guiding manned aircraft, applies Monte Carlo simulation to generate a dataset of simulated operational processes that cover the entire taxiing guidance process, and introduces three major risk assessment indicators based on the motion process between the unmanned driving guiding vehicle and the manned aircraft during the taxiing guidance process. Through the normalization function of risk evaluation indicator weights and based on the Gaussian distribution that satisfies the normal distribution, a qualitative evaluation of risk levels is conducted based on quantifiable actual operational processes. The results show that quantifiable risk assessment indicators can provide risk evaluation results with stronger real-time reference and offer operable solutions for risk avoidance.

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Human-Machine Interaction Efficiency Factors in Flight Simulator Training Towards Chinese Pilots

Cited by 5 publications

References 12 publications

Artificial Intelligence in Pilot Training and Education – Towards a Machine Learning Aided Instructor Assistant for Flight Simulators

Artificial Intelligence in Pilot Training and Education – Towards a Machine Learning Aided Instructor Assistant for Flight Simulators

Prediction of Training Cost and Difficulty for Aircraft-Type Transition Based on Similarity Assessment

Monte Carlo Simulation-Based Risk Assessment for Unmanned Ground Equipment Taxiing Guidance

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