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。 在本研究中，PI将开发一种统计方法，用于预测操作员对长期系统使用的适应性。 测试台应用程序提供了具体的框架，为发展的方法将来自驾驶领域，其中一些安全为基础的系统已经安装。 驾驶涉及驾驶员、车辆和环境之间的复杂互动，这些互动中的任何故障都可能破坏驾驶安全。 这一目标将通过五个主要组成部分实现：开发受技术影响的操作员适应行为的代表性模型;通过比较短期自适应巡航控制（ACC）用户的现场操作测试与长期ACC用户的道路研究的响应，量化操作员适应系统安全的启动因素;通过对长期使用ACC的驾驶员进行调查，了解中介因素和感知如何影响适应性行为;使用旨在捕捉碰撞的驾驶模拟器研究，识别自适应行为可能产生负面后果的安全关键情况可能发生的事件;以及开发一个适应性预测模型，该模型是由基于时间的回归模型的长期系统使用引起的。 这个项目的智力价值集中在理论和数据的整合，以提高我们对适应性策略的认识，以及它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先前的工作，并证明为什么需要客观和主观的测量来进一步了解一个人的行为如何在与智能系统交互时发生变化。 为了了解人们如何以及为什么适应不断变化的创新技术所提供的信息，需要了解用户的意图和动机; PI计划用传统上限制在人为因素研究中的时间依赖性分析来填补这一空白。 项目成果将推进我们对自适应行为过程的了解，从而可以预测那些抵消安全系统预期效益的行为，从而设计出更稳健、更有效和更高效的系统。 考虑到驾驶员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。 这项研究将连接工程，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。 研究活动将被整合到PI开发的两门研究生课程中，重点是设计以人的表现为中心的系统，以及人因工程的分析方法，PI还计划为K-12学生以及青少年司机开展各种推广活动。

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.