Predicting Driving Safety in Advancing Age

描述(由申请人提供)：这个翻译研究项目的广泛目标是通过对现实世界中较长时间段的自然驾驶的综合测量来提高对老年司机安全的预测。到目前为止，这个研究项目和团队已经开发了广泛的工具，包括神经心理测试、驾驶模拟和仪表化车辆，在预测驾驶员安全方面具有明显的优势。然而，司机在受控测试中的表现可能不同于他们在现实世界的意外情况和风险中在较长时间框架内的表现。意识到自己的功能障碍的司机可能会战略性地减少他们面临的驾驶风险，而那些缺乏意识的司机则不会。更好地了解真实世界的司机暴露和意识对于预测司机的安全和制定基于证据的标准以提高司机的意识、安全性、机动性和生活质量是必不可少的。为了解决这些关键问题，一个 多学科专家团队(神经学、认知科学、驾驶员评估、人为因素、测量、生物统计学和公共政策)将应用传感器和蜂窝通信技术的进步来实现4个具体目标：(1)通过对120名老年驾驶员进行延长时间框架的全面自然驾驶评估，量化现实世界的驾驶行为，这些老年驾驶员因年龄增长而出现一系列功能损害，从而增加发生驾驶安全错误的风险；(2)量化暴露于现实世界驾驶风险的风险；(3)量化对损害的自我意识；以及(4)开发结合功能性和自然主义驾驶数据的模型，以预测后续的撞车和交通引用。将使用现代仪器和遥测软件包对真实驾驶进行纵向研究，从一年开始的两个3个月期间，提供每个司机自己车辆的直接、详细的行为信息。总计60年的真实驾驶数据提供了驾驶员战略、战术和暴露在道路风险中的全面观察，这是任何其他来源都无法获得的。安全关键行为和错误将通过分析来自每个司机车辆的电子传感器和视频数据来识别。这种方法、方法和工具对于较老的驾驶员研究领域和广义而言都是新的。通过在真实世界环境中进行认知和行为研究，这项研究将提供关于驾驶员暴露和安全错误的独特数据，并推动NIH优先进行神经科学方面的翻译研究。本研究周期中使用的创新工具和技术将提供必要的关键信息，以确定哪些人有更大的风险，有可能导致驾驶障碍导致功能障碍、缺乏意识，以及缺乏与衰老相关的补偿行为。这些信息可用于制定战略，为患者和家属提供驾驶健康方面的建议，并根据个性化药物的承诺，通过个性化干预(包括情况意识和危险避免培训)扩大安全机动性。

DESCRIPTION (provided by applicant): The broad goal of this translational research project is to improve predictions of older driver safety through comprehensive measurements of naturalistic driving over extended time frames in the real world. To date this research project and team have developed extensive tools, including neuropsychological tests, driving simulation, and instrumented vehicles, with distinct advantages for predictions of driver safety. However drivers may behave differently in controlled tests than they do over extended time frames amid the contingencies and risks of the real world. Drivers who are aware of their functional impairments may strategically reduce their exposure to driving risk, while those who lack awareness will not. A greater understanding of real-world driver exposure and awareness is indispensible to predictions of driver safety and development of evidence-based criteria to improve driver awareness, safety, mobility, and quality of life. To tackle these linchpin issues, a multidisciplinary team of experts (in neurology, cognitive science, driver assessment, human factors, measurement, biostatistics, and public policy) will apply advances in sensor and cellular communications technology to meet 4 Specific Aims: (1) Quantify real-world driving behavior through comprehensive naturalistic driving assessments over extended time frames in 120 older drivers who are at increased risk for driving safety errors because of a range of functional impairment associated with aging;(2) Quantify exposure to real-world driving risks; (3) Quantify self-awareness of impairment; and (4) Develop models that incorporate functional and naturalistic driving data to predict subsequent crashes and traffic citations. Real-life driving wil be studied longitudinally using modern instrumentation and telemetry packages providing direct, detailed information on behavior from each driver's own vehicle over two 3-month periods starting one year apart. The grand total of 60 years of real-life driving data provides comprehensive observations of driver strategy, tactics and exposure to road risks not available from any other source. Safety-critical behaviors and errors will be identified through analyses of electronic sensor and video data from each driver's vehicle. The approach, methodologies, and instrumentation are novel to the field of older driver research and in a broad sense. By tackling cognitive and behavioral research in real-world settings, this study will provide unique data on driver exposure and safety errors and advance the NIH priority of performing translational research in neuroscience. Innovative tools and techniques used in this study cycle will provide critical information needed to identify individuals who are at greater risk for impaired driving du to functional impairments, lack of awareness, and lack of compensatory behaviors associated with aging. The information could be used to develop strategies for advising patients and families on fitness to drive, and extend safe mobility through individualized interventions (including situation awareness and hazard avoidance training), in line with the promise of personalized medicine.