Predicting Driving Safety in Advancing Age

预测高龄驾驶安全

• 批准号: 9508331
• 负责人: MATTHEW RIZZO
• 金额: $ 11.56万
• 依托单位: UNIVERSITY OF NEBRASKA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2019-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9508331
• 关键词: Activities of Daily Living; Affect; Aging; Attenuated; Automobile Driving; Awareness; Behavior; Behavioral Research; Biometry; Cognitive; Cognitive Science; Communication; Communities; Data; Demographic Factors; Development; Elderly; Exposure to; Family; Future; Goals; Guidelines; Home environment; Human; Impairment; Individual; Intervention; Knowledge; Laboratories; Life; Light; Longitudinal Studies; Measurement; Medical; Methodology; Mind; Modeling; Modernization; 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; Standardization; Taxonomy; Techniques; Technology; Teenagers; 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; public health relevance; screening; sensor; simulation; therapy development; tool

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）：该转化研究项目的总体目标是通过对现实世界中较长时间范围内的自然驾驶进行全面测量来改进对老年驾驶员安全的预测。迄今为止，该研究项目和团队已经开发了广泛的工具，包括神经心理学测试、驾驶模拟和仪表车辆，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在现实世界的意外事件和风险中长时间框架内的表现有所不同。意识到自己功能障碍的驾驶员可以策略性地减少驾驶风险，而缺乏意识的驾驶员则不会。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和制定基于证据的标准以提高驾驶员意识、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， 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）将应用先进的传感器和蜂窝通信技术来实现 4 个具体目标：(1) 通过以下方式量化现实世界的驾驶行为对 120 名老年驾驶员在较长时间内进行全面的自然驾驶评估，这些驾驶员由于与衰老相关的一系列功能障碍而面临更高的驾驶安全错误风险；(2) 量化现实世界的驾驶风险； (3) 量化自我损伤意识； (4) 开发包含功能和自然驾驶数据的模型，以预测随后的碰撞和交通罚单。将使用现代仪器和遥测包对现实生活中的驾驶进行纵向研究，提供有关每个驾驶员自己的车辆在相隔一年的两个三个月期间的行为的直接、详细信息。总计 60 年的现实驾驶数据提供了对驾驶员策略、策略和道路风险的全面观察，这是任何其他来源所无法提供的。通过分析每个驾驶员车辆的电子传感器和视频数据，可以识别安全关键行为和错误。从广义上讲，该方法、方法和仪器对于老年驾驶员研究领域来说是新颖的。通过解决现实环境中的认知和行为研究，这项研究将提供有关驾驶员暴露和安全错误的独特数据，并推进 NIH 在神经科学领域进行转化研究的优先事项。本研究周期中使用的创新工具和技术将提供所需的关键信息，以识别因功能障碍、缺乏意识和缺乏与衰老相关的补偿行为而面临更大驾驶障碍风险的个人。这些信息可用于制定策略，为患者和家属提供有关驾驶健康的建议，并通过个性化干预措施（包括态势感知和危险规避培训）扩大安全出行，这符合个性化医疗的承诺。