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

职业：模拟操作员自适应行为对系统安全的影响

• 批准号: 1027609
• 负责人: Linda Boyle
• 金额: $ 28.66万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-10-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1027609&HistoricalAwards=false
• 关键词: CAREER; Modeling; effect; operators; adaptive

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.

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，操作员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期系统使用的适应情况。 为该方法的开发提供具体框架的测试台应用程序将源自驾驶领域，其中已经安装了许多基于安全的系统。 驾驶涉及驾驶员、车辆和环境之间复杂的交互，这些交互中的任何故障都会损害驾驶安全。 该目标将通过五个主要组成部分来实现：开发受技术影响的操作员自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响应与长期 ACC 用户的道路研究的响应进行比较，量化操作员适应系统安全的启动因素；通过向长期使用 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；使用旨在捕获可能发生碰撞的事件的驾驶模拟器研究来识别自适应行为可能产生负面后果的安全关键情况；开发适应预测模型，该模型是由于系统长时间使用基于时间的回归模型而产生的。 该项目的智力价值集中在理论和数据的整合上，以增进我们对自适应策略及其如何以意想不到的方式影响系统使用的了解。 该研究将扩展 PI 之前的工作，并证明为什么需要客观和主观测量来进一步了解一个人在与智能系统交互时的行为如何变化。 为了了解人们如何以及为何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 计划通过依赖时间的分析来填补这一空白，而这种分析传统上仅限于人为因素研究。 项目成果将增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效益的行为，从而设计出更稳健、更有效、更高效的系统。 更广泛的影响：考虑到驾驶员策略变化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。 研究活动将被纳入 PI 开发的两门研究生课程中，重点是设计以人类表现为中心的系统和人因工程学的分析方法，并且 PI 进一步计划了针对 K-12 学生和青少年司机的各种外展活动。