驾驶模拟器中的横向控制:与神经心理学测试和道路安全错误的相关性。

Amy M. Johnson, J. Dawson, M. Rizzo
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引用次数: 12

摘要

驾驶模拟器以高捕获率提供车辆位置的精确信息。为了分析这些数据,我们之前提出了一个时间序列模型,该模型将侧位数据简化为几个参数,用于测量侧位控制,并表明这些参数可以检测神经受损和健康驾驶员之间的差异(Dawson et al ., 2010a)。在本文中,我们关注该模型的“重新定心”参数,并测试参数估计是否与越野神经心理测试和/或道路安全误差相关。我们对127名年龄在40至89岁之间的神经健康司机进行了评估。我们发现我们的重新定位参数与五项神经心理测试有显著的相关性:线方向判断(r = 0.38)、块设计(r = 0.27)、对比敏感度(r = 0.31)、近视灵敏度(r = -0.26)和凹槽钉板(r = -0.25)。我们还发现,我们的重新定心参数与停车标志时的道路安全误差(r = -0.34)和转弯时的道路安全误差(r = -0.22)有关。这些结果表明,我们的重新定心参数可能是测量和监测车辆保持侧向控制能力的有用工具。随着基于gps的技术在测量车辆定位的精度和可靠性方面不断提高,我们的时间序列模型可能会被应用于对认知能力下降敏感的现实世界中驾驶员表现的自动指标。这项工作得到了NIH/NIA奖AG17177、AG15071和NS044930的支持,并获得了日产汽车公司的奖学金。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
LATERAL CONTROL IN A DRIVING SIMULATOR: CORRELATIONS WITH NEUROPSYCHOLOGICAL TESTS AND ON-ROAD SAFETY ERRORS.
SUMMARY Driving simulators provide precise information on vehicular position at high capture rates. To analyze such data, we have previously proposed a time series model that reduces lateral position data into several parameters for measuring lateral control, and have shown that these parameters can detect differences between neurologically impaired and healthy drivers (Dawson et al, 2010a). In this paper, we focus on the "re-centering" parameter of this model, and test whether the parameter estimates are associated with off-road neuropsychological tests and/or with on-road safety errors. We assessed such correlations in 127 neurologically healthy drivers, ages 40 to 89. We found that our re-centering parameter had significant correlations with five neuropsychological tests: Judgment of Line Orientation (r = 0.38), Block Design (r = 0.27), Contrast Sensitivity (r = 0.31), Near Visual Acuity (r = -0.26), and Grooved Pegboard (r = -0.25). We also found that our re-centering parameter was associated with on-road safety errors at stop signs (r = -0.34) and on-road safety errors during turns (r = -0.22). These results suggest that our re-centering parameter may be a useful tool for measuring and monitoring ability to maintain vehicular lateral control. As GPS-based technology continues to improve in precision and reliability to measure vehicular positioning, our time-series model may potentially be applied as an automated index of driver performance in real world settings that is sensitive to cognitive decline. This work was supported by NIH/NIA awards AG17177, AG15071, and NS044930, and by a scholarship from Nissan Motor Company.
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