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

Sara A. Wilkins
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引用次数: 8

Abstract

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.
对单飞行员通用航空作业的认知辅助对飞行员的益处的检查
评估新驾驶舱技术的人为因素是一项耗时且昂贵的任务。一个典型的人在循环中的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技术变化和要测试的场景设置严格的限制,并绘制出迭代设计过程。然而,这些研究确保了飞行甲板技术是高效的,有效的,并且从飞行员精神努力的角度来看,是可管理的。虽然不建议也不希望消除人在循环评估,但可以减少设计周期数量并帮助人在循环评估针对最有前途的设计概念的方法是有价值的。其中一种方法是对飞行员活动进行快速模拟,利用人类行为建模来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、操作符、方法和选择规则(GOMS),以及工作记忆和心理努力的理论,能够提供任务的时间和心理努力的估计[1]。MITRE公司为此开发了一种工具,这是一种认知计算器或“调节器”。本文描述了一个例子,使用Cogulator在有或没有飞行员导向的认知助手(称为数字副驾驶)的情况下对心理努力进行建模。数字副驾驶是由MITRE公司赞助的联邦航空管理局开发的,是一个工作原型,可以及时向飞行员提供上下文信息和提醒[2]。本研究比较了使用调节器的两种不同的操作模式:在没有数字副驾驶和有数字副驾驶的情况下,单个飞行员在飞行进近阶段的想法和行动。Cogulator使用每种操作模式下的任务分解来估计三个指标:飞行员的任务时间、头向下时间和工作记忆负荷。任务时间是描述完成任务所需时间的汇总统计信息。低头时间是指飞行员在驾驶舱内而不是在驾驶舱外观察信息的时间。工作记忆负荷是一种认知结构,在这种结构中,信息被暂时存储并被操纵以完成复杂的任务[3]。本文介绍了调节器、评价方法,并对结果进行了比较。结果显示了在飞行进近阶段的任务完成时间、头部下降时间和工作记忆负荷,包括以下五项任务:检查自动终端信息服务(ATIS)频率、接收上下文频率、审查并遵循清单、确定塔台是否开放以及确定首选跑道。在这五个任务模型中,结果表明,除了检查ATIS频率外,数字副驾驶在所有任务中都节省了时间;节省所有任务的时间;工作记忆负荷节省或所有任务都没有变化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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