Predicting Driving Safety in Advancing Age

描述（由申请人提供）：这个转化研究项目的广泛目标是通过全面测量真实的世界中延长时间范围内的自然驾驶来改善对老年驾驶员安全性的预测。到目前为止，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，驾驶模拟和仪表化车辆，在预测驾驶员安全方面具有明显的优势。然而，在真实的世界的突发事件和风险中，驾驶员在受控测试中的行为可能与他们在延长的时间框架内的行为不同。意识到自己功能障碍的驾驶员可能会从战略上减少驾驶风险，而缺乏意识的驾驶员则不会。更好地了解现实世界中的驾驶员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不可或缺的。为了解决这些关键问题， 多学科专家组（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，以满足4个具体目标：（1）量化真实的-通过对120名年龄较大的驾驶员在较长时间内进行全面的自然驾驶评估，了解世界驾驶行为，这些驾驶员因以下原因而面临驾驶安全错误的风险增加一系列与衰老相关的功能障碍;（2）量化暴露于现实世界的驾驶风险;（3）量化自我意识的损害;（4）开发模型，将功能和自然驾驶数据结合起来，以预测随后的碰撞和交通引用。现实生活中的驾驶将被纵向研究，使用现代仪器和遥测包提供直接的，详细的信息，从每个司机自己的车辆行为超过两个3个月的时间间隔一年。总计60年的真实驾驶数据提供了对驾驶员策略，战术和道路风险的全面观察，而这些都是其他来源所无法提供的。安全关键行为和错误将通过分析每个驾驶员车辆的电子传感器和视频数据来识别。的方法，方法和仪器是新的老司机的研究领域，并在广泛的意义上。通过解决现实世界中的认知和行为研究，这项研究将提供关于驾驶员暴露和安全错误的独特数据，并推进NIH在神经科学中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识别因功能障碍、缺乏意识以及缺乏与衰老相关的补偿行为而导致驾驶障碍风险更大的个体所需的关键信息。这些信息可用于制定策略，为患者和家庭提供驾驶健康方面的建议，并通过个性化干预措施（包括情境意识和危险规避培训）延长安全流动性，符合个性化医疗的承诺。

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.