CAREER: Modeling the effect of operators' adaptive behavior on system safety

许多技术创新旨在通过简化安全关键情况下的任务和用户需求来提高操作员的安全性。然而，运营商实际上可能会以意想不到的方式适应这些系统，这可能会抵消预期的结果。在这项研究中，PI将开发一种统计方法来预测操作员对长时间系统使用的适应性。为开发方法提供具体框架的试验台应用程序将来自驾驶领域，其中已经安装了许多基于安全的系统。驾驶涉及驾驶员、车辆和环境之间的复杂交互作用，这些交互作用中的任何故障都可能破坏驾驶安全。目标将通过五个主要部分实现：开发受技术影响的操作员适应行为的代表性模型；通过将短期自适应巡航控制(ACC)用户的现场操作测试的反应与长期ACC用户的道路研究进行比较，量化操作员适应系统安全的启动因素；通过分配给长期使用ACC的司机的调查，了解中介因素和看法如何影响适应行为；使用旨在捕捉碰撞可能事件的驾驶模拟器研究，识别适应行为可能产生负面后果的安全关键情况；以及开发一种适应的预测模型，这是由于系统长期使用基于时间的回归模型而产生的。这个项目的智力价值集中在理论和数据的集成上，以促进我们对适应策略的了解，以及这如何以意想不到的方式影响系统的使用。这项研究将扩展PI的先前工作，并说明为什么需要客观和主观措施来进一步了解一个人在与智能系统交互时行为是如何变化的。为了了解人们如何以及为什么适应不断变化的创新技术提供的信息，需要了解用户的意图和动机；PI计划用传统上受限于人的因素研究的时间相关分析来填补这一空白。项目成果将促进我们对适应性行为过程的了解，从而可以预测那些抵消安全系统预期好处的行为，从而允许设计出更健壮、有效和高效的系统。更广泛的影响：考虑到司机不断变化的策略的系统将更有效地减少世界上的撞车和死亡人数。这项研究将把工程学、计量经济学、统计学和流行病学中使用的分析技术的应用范围联系起来。研究活动将被纳入国际司机协会开发的两门研究生课程，这两门课程的重点是设计以人的表现为中心的系统，以及人因工程中的分析方法，该协会还计划为K-12学生以及青少年司机举办各种外联活动。

Many technological innovations are designed to increase operator safety by simplifying tasks and user demands in safety-critical situations. However, operators may actually adapt to these systems in unexpected ways that may counteract the intended outcome. In this research, the PI will develop a statistical methodology for predicting operator adaptation to prolonged system use. The test bed application providing the concrete framework for development of the methodology will derive from the driving domain, where a number of safety based systems have already been installed. Driving involves complex interactions between the driver, the vehicle, and the environment, and any breakdowns in these interactions can undermine driving safety. The objective will be achieved through five major components: development of a representative model of operators' adaptive behavior as influenced by technology; quantification of the initiating factors of operator adaptation on system safety by comparing the responses from a field operation test of short term adaptive cruise control (ACC) users with an on-road study of long-term ACC users; acquisition of an understanding how mediating factors and perceptions influence adaptive behavior using surveys distributed to drivers who have used ACC for extended periods; identification of the safety critical situations where adaptive behavior may have negative consequences using a driving simulator study designed to capture collision likely events; and development of a predictive model of adaptation that results from prolonged system use with time-based regression models. The intellectual merit of this project centers on the integration of theory and data to advance our knowledge of adaptive strategies and how that influences system use in unintended ways. The research will extend prior work of the PI and demonstrate why objective and subjective measures are needed to further understand how a person's behavior changes as they interact with intelligent systems. To understand how and why people adapt to information provided by innovative technologies that are continually changing, an understanding of the user's intentions and motivations is needed; the PI plans to fill this gap with time-dependent analyses which have traditionally been limited in human-factors research. Project outcomes will advance our knowledge of the adaptive behavior process, such that those behaviors that counteract the intended benefits of safety systems can be predicted and therefore allow for more robust, effective, and efficient systems to be designed.Broader Impact: Systems that account for the changing strategies of drivers will be more effective in reducing the number of crashes and fatalities in the world. This research will connect the range of applications for analytical techniques used across engineering, econometrics, statistics, and epidemiology. The research activities will be integrated into two graduate courses developed by the PI that focus on designing systems centered on human performance, and analytical methods in human factors engineering, and the PI further plans a variety of outreach activities intended for K-12 students as well as teenage drivers.