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

自动驾驶汽车动态信任对驾驶员行为的影响的测量和建模

• 批准号: 2310621
• 负责人: Anthony McDonald
• 金额: $ 64.81万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2310621&HistoricalAwards=false
• 关键词: Measuring; Modeling; Impact; Dynamic; Trust

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.

机动车碰撞每年造成35 000多人死亡，近300万人受伤。自动驾驶汽车技术已经成为一种有前途的机制，以防止这些碰撞，增加个人流动性，并降低排放。由于公众对自动驾驶汽车安全性的担忧日益增加，特别是在自动驾驶系统和人类驾驶员之间转移控制权的过程中，这些承诺的实现受到了限制。人类驾驶员和自动化系统之间的信任是这些交互过程中的核心问题。之前的研究表明，人机系统的安全性和性能需要校准的信任，即人类驾驶员对自动化系统的信任与系统的能力相匹配。自动驾驶汽车中的信任校准是一个难以捉摸的挑战，因为信任测量和方法的局限性，阐明了技术设计决策对信任和驾驶员行为的影响。该项目将通过促进对人类自动化信任的理解来促进科学的进步和促进国民健康。具体而言，该项目将解决现有信任措施的局限性，模型信任和驾驶员行为，并确定包含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，可大大减少与车辆碰撞有关的人员伤亡。这项工作的更广泛的影响包括本科生和研究生课程的开发，为德克萨斯州农工大学代表性不足的本科生提供重点研究机会&，以及为当地高中生提供学生腿外展活动。该项目将包括三个阶段，旨在（1）开发一种新颖客观的动态信任测量方法，使用AV交互期间神经激活的实时测量方法，（2）沿着动态信任的频谱对驾驶员与自动化车辆的交互进行建模，以及（3）利用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。测量开发将得到来自人类受试者研究的数据的支持，其中模拟自动驾驶车辆中的驾驶员将遇到一系列旨在调节对系统的信任的现实驾驶场景，同时收集生理，神经，行为和主观测量。神经学数据将使用回归和连接分析方法进行分析，以识别与通过生理和主观测量验证的信任状态的相关性。这些关联将用于训练和测试新的驱动程序信任和对自动化事件的响应的过程模型，这些模型扩展了现有的驱动程序决策和控制框架。该模型和测量将在第二次驾驶模拟研究中进行验证，包括信任校准干预。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。