多模式增强现实技术在航空交通程序培训中的应用

Birgit Moesl, Harald Schaffernak, Wolfgang Vorraber, R. Braunstingl, I. Koglbauer
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引用次数: 1

摘要

空中碰撞是通用航空领域最大的安全隐患之一。本研究解决了根据视觉飞行规则(VFR)训练交通程序的多模式增强现实(AR)应用的描述和实验评估。AR有潜力通过弥合理论与实践之间的差距来补充传统的飞行教学,并通过释放与有限的模拟器时间相关的学生的时间和表现压力。然而,评估AR在特定领域的影响并识别任何潜在的负学习迁移是至关重要的。开发了多模式增强现实应用程序,以解决各种培训领域的问题:正确扫描模式的指导和反馈,估计遇到的车辆是否在碰撞路线上,以及相关规则的应用。AR应用程序还为碰撞检测、避免和优先级决策提供性能反馈。实验评估将59名受训者(28名女性,31名男性)分为实验组(AR训练)和对照组(模拟器训练)。在飞行模拟器中不使用增强现实技术的测试结果表明,使用增强现实技术训练的小组获得了与对照组相似的表现水平。在训练绩效、工作量、情境感知、情绪和动机方面,AR均无负向训练效应。训练后,任务被认为不那么具有挑战性,碰撞检测的准确性得到了提高,受训人员报告的负面情绪和对失败的恐惧也减少了。此外,AR扫描模式测试显示,AR训练组的扫描模式表现明显优于对照组。此外,性别对AR功能的情绪、动机和偏好有显著影响,但对表现没有影响。女性比男性更喜欢AR语音交互和指南针全息图。男性比女性更喜欢交通全息图和AR投影场。这些结果很重要,因为它们为多模式AR应用的好处提供了实验证据,可以用来补充飞行模拟器训练。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multimodal Augmented Reality Applications for Training of Traffic Procedures in Aviation
Mid-air collision is one of the top safety risks in general aviation. This study addresses the description and experimental assessment of multimodal Augmented Reality (AR) applications for training of traffic procedures in accordance with Visual Flight Rules (VFR). AR has the potential to complement the conventional flight instruction by bridging the gap between theory and practice, and by releasing students’ time and performance pressure associated with a limited simulator time. However, it is critical to assess the impact of AR in the specific domain and to identify any potential negative learning transfer. Multimodal AR applications were developed to address various areas of training: guidance and feedback for the correct scanning pattern, estimation if an encountering traffic is on collision course and application of the relevant rules. The AR applications also provided performance feedback for collision detection, avoidance and priority decisions. The experimental assessment was conducted with 59 trainees (28 women, 31 men) assigned to an experimental group (AR training) and a control group (simulator training). The results of tests without AR in the flight simulator show that the group that trained with AR obtained similar levels of performance like the control group. There was no negative training effect of AR on trainees’ performance, workload, situational awareness, emotion nor motivation. After training the tasks were perceived as less challenging, the accuracy of collision detection has improved, and the trainees reported less intense negative emotions and fear of failure. Furthermore, a scanning pattern test in AR showed that the AR training group performed the scanning pattern significantly better than the control group. In addition, there was a significant gender effect on emotion, motivation and preferences for AR features, but not on performance. Women liked the voice interaction with AR and the compass hologram more than men. Men liked the traffic holograms and the AR projection field more than women. These results are important because they provide experimental evidence for the benefits of multimodal AR applications that could be used complementary to the flight simulator training.
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