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Examination of pilot benefits from cognitive assistance for single-pilot general aviation operations

机译:检查飞行员对单机通用航空运营的认知益处

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Evaluating the human factors of new cockpit technologies is a time consuming and expensive task. A typical human-in-the-loop evaluation can take several months to design, execute, and document. Moreover, retaining statistical power requires placing hard limits on the technological variations and scenarios to be tested, drawing out the iterative design process. These studies, however, ensure that flight deck technologies are efficient, effective, and, from a pilot mental effort perspective, manageable. While eliminating human-in-the-loop evaluation is neither recommended nor desired, methods that can reduce the number of design cycles and help human-in-the-loop evaluations target the most promising design concepts are valuable. One such method is fast-time simulation of the pilot activities to assess task times and working memory load using human performance modeling. Well-established task analysis methodologies, i.e., Goals, Operators, Methods, and Selection Rules (GOMS), with theories of working memory and mental effort are able to provide estimates of temporal and mental effort for a task [1]. The MITRE Corporation developed a tool for this purpose, which is a cognitive calculator or “Cogulator.” This paper describes an example of using Cogulator for modeling mental effort with and without a pilot-oriented cognitive assistant, called the Digital Copilot. The Digital Copilot, developed through sponsorship from the Federal Aviation Administration by The MITRE Corporation, is a working prototype that provides contextual information and reminders to the pilot in a timely manner [2]. This study compares two different modes of operation using Cogulator: a single pilot's thoughts and actions during the approach phase of flight without the Digital Copilot and another with the Digital Copilot. Cogulator uses the task decompositions in each mode of operation to estimate three metrics: the pilot's task times, heads down times, and working memory load. Task time is a summary statistic that describes the amount of time that elapsed for a task to be completed. Heads down time is the amount of time that a pilot spends looking at information within the cockpit instead of outside. Working memory load is the cognitive construct in which information is temporarily stored and manipulated to complete complex tasks [3]. The paper describes Cogulator, the evaluation method, and the results of the comparison. Results are presented in terms of task completion time, heads down time, and working memory load during the approach phase of flight for the following five tasks: check automatic terminal information service (ATIS) frequency, receive contextual frequency, review and follow a checklist, determine if the tower is open, and determine the preferred runway. Of these five tasks modeled, results show that the Digital Copilot provides time savings in all tasks except for check ATIS frequency; heads down time savings for all tasks; and working memory load savings or no change for all tasks.
机译:评估新驾驶舱技术的人为因素是一项耗时且昂贵的任务。典型的在环评估可能需要几个月的时间来设计,执行和记录。此外,保持统计能力需要对要测试的技术变化和方案设置严格的限制,从而制定出迭代的设计过程。但是,这些研究确保驾驶舱技术高效,有效,并且从飞行员的脑力劳动角度来看,是可控的。尽管既不建议也不希望消除在环评估,但是可以减少设计周期数并帮助环在评估针对最有前途的设计概念的方法非常有价值。一种这样的方法是使用人员绩效模型对飞行员活动进行快速仿真,以评估任务时间和工作记忆负荷。完善的任务分析方法,即目标,操作员,方法和选择规则(GOMS),以及工作记忆和脑力劳动理论,能够为任务提供时间和脑力劳动估计[1]。 MITRE公司为此目的开发了一种工具,它是一种认知计算器或“ Cogulator”。本文介绍了一个示例,该示例使用Cogulator建模带有或不带有面向飞行员的认知助手(称为Digital Copilot)的心理努力。由MITER Corporation由联邦航空管理局赞助开发的数字副驾驶员是一种工作原型,可以及时向飞行员提供上下文信息和提醒[2]。这项研究比较了使用Cogulator的两种不同的操作模式:在没有数字副驾驶的情况下,一名飞行员在进近飞行阶段的想法和行动,而另一种在使用数字副驾驶的情况下。凝结器在每种操作模式下使用任务分解来估计三个指标:飞行员的任务时间,降落时间和工作内存负载。任务时间是一个摘要统计信息,它描述完成任务所花费的时间。平视时间是指飞行员在座舱内部而不是外部区域查看信息所花费的时间。工作记忆负荷是一种认知结构,其中信息被暂时存储和操纵以完成复杂的任务[3]。本文介绍了Cogulator,评估方法和比较结果。结果以下列五个任务的任务完成时间,停机时间和进近飞行阶段的工作内存负荷的形式表示:检查自动终端信息服务(ATIS)频率,接收上下文频率,查看并遵循清单,确定塔架是否打开,并确定首选跑道。在建模的这五个任务中,结果表明,数字副驾驶在所有任务中都节省了时间,除了检查ATIS频率外。节省所有任务的时间;并节省工作内存负载,或者对所有任务不变。

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