自主约束飞行计划中透明度对飞行员信任和协议的影响

Garrett G. Sadler, Henri Battiste, N. Ho, Lauren C. Hoffmann, W. Johnson, R. Shively, J. Lyons, David E. Smith
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引用次数: 34

摘要

我们进行了一项“人在循环”研究,以探索透明度在高度自动化系统中产生信任和依赖方面的作用。具体来说,我们研究了透明度如何影响对自主约束飞行计划(ACFP)的信任和依赖,ACFP是NASA减少机组人员操作(RCO)概念的一部分,正在开发的关键自动化系统。ACFP的目的是提供一个增强的地面操作员,称为超级调度员,当飞机的主要目的地不可用时,建议飞机改道。在目前的研究中,12名扮演超级调度员角色的商业运输飞行员被给予6个时间紧迫的“全陆地”场景,他们需要使用ACFP来决定多架飞机的改道。两个因素被操纵。最主要的因素是透明度。在低透明度的情况下,飞行员得到了一个推荐的机场和跑道,以及关于天气状况、飞机类型、该机场和其他机场的机场和跑道特征的基本信息。在适度透明的情况下,飞行员还会得到推荐机场的风险评估,如果他们提出要求,也会得到其他机场的风险评估。在高透明度情况下,向飞行员提供了额外信息,包括风险评估的理由。次要因素是风险水平,或高或低。对于高风险的飞机,所有可能的改道都被评为高风险,ACFP为糟糕的情况提供了最佳选择。对于低风险的飞机,ACFP只给飞行员提供了低风险的选择。收集了主观和客观指标,包括评级信任,飞行员是否检查自动化推荐的有效性,飞行员是否最终飞往推荐的备降机场。主要结果表明:1)透明度越高,飞行员的信任度越高;2)透明度低和风险高时,飞行员更有可能验证ACFP的建议;3)透明度低和风险高时,飞行员更有可能探索ACFP的其他选择;4)接受或拒绝ACFP建议的决定随着解释透明度的提高而增加。研究发现,透明度越高,信任度越高,核实其他选择的需求越低,对ACFP建议的认同程度越高,这证实了透明度在帮助依赖自动化建议方面的重要性。对通过调查和汇报面谈从研究对象收集的定性数据的进一步分析也为ACFP的新设计建议提供了基础。
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Effects of transparency on pilot trust and agreement in the autonomous constrained flight planner
We performed a human-in-the-loop study to explore the role of transparency in engendering trust and reliance within highly automated systems. Specifically, we examined how transparency impacts trust in and reliance upon the Autonomous Constrained Flight Planner (ACFP), a critical automated system being developed as part of NASA's Reduced Crew Operations (RCO) Concept. The ACFP is designed to provide an enhanced ground operator, termed a super dispatcher, with recommended diversions for aircraft when their primary destinations are unavailable. In the current study, 12 commercial transport rated pilots who played the role of super dispatchers were given six time-pressured “all land” scenarios where they needed to use the ACFP to determine diversions for multiple aircraft. Two factors were manipulated. The primary factor was level of transparency. In low transparency scenarios the pilots were given a recommended airport and runway, plus basic information about the weather conditions, the aircraft types, and the airport and runway characteristics at that and other airports. In moderate transparency scenarios the pilots were also given a risk evaluation for the recommended airport, and for the other airports if they requested it. In the high transparency scenario additional information including the reasoning for the risk evaluations was made available to the pilots. The secondary factor was level of risk, either high or low. For high-risk aircraft, all potential diversions were rated as highly risky, with the ACFP giving the best option for a bad situation. For low-risk aircraft the ACFP found only low-risk options for the pilot. Both subjective and objective measures were collected, including rated trust, whether the pilots checked the validity of the automation recommendation, and whether the pilots eventually flew to the recommended diversion airport. Key results show that: 1) Pilots' trust increased with higher levels of transparency, 2) Pilots were more likely to verify ACFP's recommendations with low levels of transparency and when risk was high, 3) Pilots were more likely to explore other options from the ACFP in low transparency conditions and when risk was high, and 4) Pilots' decision to accept or reject ACFP's recommendations increased as a function of the transparency in the explanation. The finding that higher levels of transparency was coupled with higher levels of trust, a lower need to verify other options, and higher levels of agreement with ACFP recommendations, confirms the importance of transparency in aiding reliance on automated recommendations. Additional analyses of qualitative data gathered from subjects through surveys and during debriefing interviews also provided the basis for new design recommendations for the ACFP.
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