Predicting Driving Safety in Advancing Age

预测高龄驾驶安全

• 批准号: 8857182
• 负责人: MATTHEW RIZZO
• 金额: $ 53.56万
• 依托单位: UNIVERSITY OF NEBRASKA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8857182
• 关键词: Activities of Daily Living; Adolescence; Affect; Aging; Attenuated; Automobile Driving; Awareness; Behavior; Behavioral Research; Biometry; Cognitive; Cognitive Science; Communication; Communities; Data; Databases; Demographic Factors; Development; Elderly; Electronics; Exposure to; Family; Future; Goals; Guidelines; Health; Home environment; Human; Impairment; Individual; Intervention; Intervention Trial; Knowledge; Laboratories; Life; Light; Measurement; Methodology; Mind; Modeling; Neurology; Neuropsychological Tests; Neurosciences; Outcome; Patients; Pattern; Performance; Public Policy; Quality of life; Recommendation; Recording of previous events; Records; Research; Research Project Grants; Risk; Safety; Sampling; Self Perception; Source; Taxonomy; Techniques; Technology; Telemetry; Testing; Time; Training; Translational Research; United States National Institutes of Health; Weather; age related; aging brain; base; cohort; density; design; driving behavior; evidence base; fitness; follow-up; functional disability; hazard; impaired driving performance; improved; innovation; instrument; instrumentation; interdisciplinary approach; meetings; motor impairment; multidisciplinary; novel; older driver; personalized medicine; screening; sensor; simulation; therapy development; tool; trafficking

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.

描述（由申请人提供）：这个转化研究项目的总体目标是通过在现实世界中延长时间框架的自然驾驶的综合测量来提高对老年驾驶员安全的预测。迄今为止，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，驾驶员在受控测试中的表现可能与他们在现实世界的突发事件和风险中延长时间框架的表现不同。意识到自己功能障碍的司机可能会有策略地减少自己的驾驶风险，而那些缺乏意识的司机则不会。更好地了解现实世界中驾驶员的暴露和意识对于预测驾驶员安全以及制定基于证据的标准以提高驾驶员的意识、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，a