Measuring and Modeling the Impact of Dynamic Trust in Automated Vehicles on Driver Behavior

机动车撞车事故每年造成35000多人死亡，近300万人受伤。自动车辆技术已经成为一种很有希望的机制，可以防止这些碰撞，增加个人机动性，并降低排放。由于公众对自动化车辆安全的日益担忧，这些承诺的兑现受到了限制，特别是在自动化系统和人类司机之间的控制权移交期间。在这些交互过程中，驾驶员与自动化系统之间的信任是一个核心问题。之前对人机信任的研究已经确定，人机系统的安全和性能需要经过校准的信任--一种人类驾驶员对自动化系统的信任与系统能力相匹配的状态。自动车辆中的信任校准是一个难以捉摸的挑战，因为信任测量和阐明技术设计决策对信任和驾驶员行为影响的方法存在局限性。该项目将通过促进对人-自动化信任的理解，促进科学的进步，促进国民健康。具体地说，该项目将解决现有信任措施、模型信任和驾驶员行为的局限性，并确定纳入信任校准模型的自动驾驶车辆如何影响动态信任和驾驶行为。该方法将提供指导方针和技术设计建议，可以显著减少与车辆相撞相关的人员伤亡。这项工作的广泛影响包括本科和研究生课程的发展，为德克萨斯农工大学未被充分代表的本科生提供集中的研究机会，以及面向当地高中生的学生腿拓展活动。该项目将由三个阶段组成，旨在(1)使用AV交互过程中神经激活的实时测量来开发一种新颖而客观的动态信任度量，(2)沿着动态信任的频谱模拟驾驶员与自动车辆的交互，以及(3)通过基于信任校准干预的驾驶模拟实验来验证信任度量和驾驶员行为模型。这项指标的开发将得到来自人类受试者研究的数据的支持，在这些研究中，司机在模拟的自动车辆中将遇到一系列现实的驾驶场景，这些场景旨在调整对系统的信任，同时收集生理、神经、行为和主观指标。神经学数据将用回归和连通性分析方法进行分析，以确定与通过生理和主观测量验证的信任状态相关的因素。这些关联器将用于训练和测试新的驾驶员信任和对自动化事件的响应过程模型，这些模型扩展了现有的驾驶员决策和控制框架。这些模型和措施将在包括信任校准干预在内的第二次驾驶模拟研究中得到验证。该奖项反映了NSF的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Motor vehicle crashes cause over 35,000 deaths and almost 3 million injuries per year. Automated vehicle technologies have emerged as a promising mechanism to prevent these crashes, to increase personal mobility, and to lower emissions. Delivery on these promises has been limited by a growing public concern over the safety of automated vehicles, particularly during transfers of control between automated systems and human drivers. Trust between human drivers and automated systems is a central concern during these interactions. Prior research in human-automation trust has established that the safety and performance of human-machine systems requires calibrated trust—a state where a human driver’s trust in an automated system matches the system’s capabilities. Trust calibration in automated vehicles is an elusive challenge because of limitations in trust measurement and methods that illuminate the impact of technology design decisions on trust and driver behavior. This project will promote the progress of science and advance the national health by advancing an understanding of human-automation trust. Specifically, the project will address the limitations of existing trust measures, model trust and driver behavior, and determine how autonomous vehicles that incorporate trust calibration models can influence dynamic trust and driving behavior. The approach will provide guidelines and technology design recommendations that could significantly reduce the human lives lost and injuries associated with vehicle crashes. Broader impacts of the work include undergraduate and graduate course development, focused research opportunities for underrepresented undergraduates at Texas A&M University, as well as student-leg outreach activities to local high school students.The project will consist of three phases designed to (1) develop a novel and objective measure of dynamic trust using real-time measures of neural activation during AV interactions, (2) model driver interactions with automated vehicles along the spectrum of dynamic trust, and (3) validate the trust measure and driver behavior model with a trust calibration intervention-based driving simulation experiment. The measure development will be supported by data from human subjects studies in which drivers in a simulated automated vehicle will encounter a series of realistic driving scenarios designed to modulate trust in the system while physiological, neurological, behavioral, and subjective measures are collected. Neurological data will be analyzed with regression and connectivity analysis methods to identify correlates with trust states validated by the physiological and subjective measures. The correlates will be used to train and test novel process models of driver trust and responses to automation events that extend existing driver decision-making and control frameworks. The models and measure will be validated in a second driving simulation study including a trust calibration intervention.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.