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.
Laboratory based testing often leads to attenuated predictions of human behavior in real-world settings, including in models that seek to explain factors that affect older driver safety on the road. Building on extensive findings in the previous cycle, and upon technological advances in sensor development, we are now able to observe and quantify the safety of older drivers in real- world, as they drive their own vehicles over extended time frames. The findings from the next research cycle will inform development of future tools for screening, identifying, educating, and intervening in vulnerable individuals with functional impairments due to aging in line with meeting the NIH priority of performing translational research.
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