Traffic Pattern Analysis in a Flight Simulator: Subjective and Physiological Mental Workload Assessment Techniques

Forty-eight student pilots and recently licensed private pilots were randomly assigned to one of three training groups: procedural, conceptual, and control. Participants in the procedural group spent approximately two hours reading text and watching videos specific to the step-by-step procedures of how to fly traffic patterns and land an airplane. Participants in the conceptual group spent approximately two hours reading reasoning explanations, everyday metaphors to aviation, and viewing diagrams of traffic patterns and landings. Participants in the control group spent approximately two hours watching aviation-themed videos and reading aviation-themed text, but unrelated to traffic patterns, landings, or any other flight task. During training, participants answered questions specific to the material they were reading or watching. At the conclusion of the training participants were tested on typical and atypical traffic pattern performance, typical and atypical landing performance, and routine and non routine situations for 20 minutes in a medium fidelity flight simulator. Conceptual training was best for traffic pattern performance and atypical landings. Additionally, the conceptual group had better situation awareness than the procedural and control groups for landing situations and non routine traffic pattern situations. Finally, the procedural group did not show better performance than the control group on any test.

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Risk behaviour and mental workload: Multimodal assessment techniques applied to motorbike riding simulation

Transportation Research Part F Traffic Psychology and Behaviour ◽

10.1016/j.trf.2009.02.004 ◽

2009 ◽

Vol 12 (5) ◽

pp. 361-370 ◽

Cited By ~ 66

Author(s):

Leandro L. Di Stasi ◽

Vanessa Álvarez-Valbuena ◽

José J. Cañas ◽

Antonio Maldonado ◽

Andrés Catena ◽

...

Keyword(s):

Mental Workload ◽

Risk Behaviour ◽

Multimodal Assessment ◽

Assessment Techniques

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Real-time Traffic Pattern Analysis and Inference with Sparse Video Surveillance Information

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/496 ◽

2018 ◽

Cited By ~ 4

Author(s):

Yang Wang ◽

Yiwei Xiao ◽

Xike Xie ◽

Ruoyu Chen ◽

Hengchang Liu

Keyword(s):

Real Time ◽

Video Surveillance ◽

Pattern Analysis ◽

Transition Probabilities ◽

Urban Traffic ◽

Third Party ◽

Distribution Model ◽

Traffic Patterns ◽

Traffic Pattern ◽

Real Time Traffic

Recent advances in video surveillance systems enable a new paradigm for intelligent urban traffic management systems. Since surveillance cameras are usually sparsely located to cover key regions of the road under surveillance, it is a big challenge to perform a complete real-time traffic pattern analysis based on incomplete sparse surveillance information. As a result, existing works mostly focus on predicting traffic volumes with historical records available at a particular location and may not provide a complete picture of real-time traffic patterns. To this end, in this paper, we go beyond existing works and tackle the challenges of traffic flow analysis from three perspectives. First, we train the transition probabilities to capture vehicles' movement patterns. The transition probabilities are trained from third-party vehicle GPS data, and thus can work in the area even if there is no camera. Second, we exploit the Multivariate Normal Distribution model together with the transferred probabilities to estimate the unobserved traffic patterns. Third, we propose an algorithm for real-time traffic inference with surveillance as a complement source of information. Finally, experiments on real-world data show the effectiveness of our approach.

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Unmanned aerial vehicle pilot training

10.26552/pas.z.2021.2.38 ◽

2021 ◽

Author(s):

Nikola Sklaličanová ◽

◽

Branislav Kandera

Keyword(s):

Unmanned Aerial Vehicles ◽

Unmanned Aerial Vehicle ◽

Mental Workload ◽

Pilot Training ◽

Flight Simulator ◽

Practical Training ◽

Individual Measurement ◽

Measurement Results ◽

Aerial Vehicle ◽

The Individual

The paper titled "Unmanned aerial vehicle pilot training" is focused on the analysis of unmanned aerial vehicle pilot training and the importance of using an unmanned flight simulator during the practical training of unmanned aerial vehicle pilots. For the realization of the paper, we used a device that served to measure the mental workload of unmanned aerial vehicle pilots during simulated and practical flight. Our experiment involved 5 unmanned aerial vehicle pilots in training who had zero or minimal flying experience. The aim of this work was to investigate to what extent mental workload acts on UAV pilots during simulated and practical flights. The measurements and their analysis showed that a much greater load is exerted on the pilots of unmanned aerial vehicles during practical flight. Through a primary experiment of already experienced pilots, we concluded that the majority of respondents would welcome the opportunity to use an unmanned flight simulator during their training. The paperconcludes with a summary of the individual measurement results, graphical representations of the respondents' answers, as well as an implementation design that could be applied to the training of UAV pilots.

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