University of Wyoming, Department of Civil & Architectural Engineering, 1000 E University Ave, Dept. 3295, Laramie, WY 82071, United States.
J Safety Res. 2019 Feb;68:71-80. doi: 10.1016/j.jsr.2018.12.015. Epub 2018 Dec 23.
Driving in foggy weather conditions has been recognized as a major safety concern for many years. Driver behavior and performance can be negatively affected by foggy weather conditions due to the low visibility in fog. A number of previous studies focused on driver performance and behavior in simulated environments. However, very few studies have examined the impact of foggy weather conditions on specific driver behavior in naturalistic settings.
This study utilized the second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) dataset to evaluate driver lane-keeping behavior in clear and foggy weather conditions. Preliminary descriptive analysis was conducted and a lane-keeping model was developed using the ordered logistic regression approach to achieve the study goals.
This study found that individual variables such as visibility, traffic conditions, lane change, driver marital status, and geometric characteristics, as well as some interaction terms (i.e., weather and gender, surface condition and driving experience, speed limit and mileage last year) significantly affect lane-keeping ability. An important finding of this study illustrated that affected visibility caused by foggy weather conditions decreases lane-keeping ability significantly. More specifically, drivers in affected visibility conditions showed 1.37 times higher Standard Deviation of Lane Position (SDLP) in comparison with drivers who were driving in unaffected visibility conditions.
These results provide a better understanding of driver lane-keeping behavior and driver perception of foggy weather conditions. Moreover, the results might be used to improve Lane Departure Warning (LDW) systems algorithm by allowing them to account for the effects of fog on visibility. Practical Applications: These results provide a better understanding of driver lane-keeping behavior and driver perception of foggy weather conditions. Moreover, the results might be used to improve Lane Departure Warning (LDW) systems algorithm by allowing them to account for the effects of fog on visibility.
多年来，雾天驾驶一直被认为是一个重大的安全隐患。由于雾中的能见度低，雾天会对驾驶员的行为和表现产生负面影响。以前的许多研究都集中在模拟环境中的驾驶员性能和行为上。然而，很少有研究在自然环境中考察雾天条件对特定驾驶员行为的影响。
本研究利用第二个战略公路研究计划（SHRP2）自然驾驶研究（NDS）数据集评估在晴朗和雾天条件下驾驶员的车道保持行为。进行了初步描述性分析，并使用有序逻辑回归方法开发了一个车道保持模型，以实现研究目标。
本研究发现，个体变量，如能见度、交通状况、车道变换、驾驶员婚姻状况和几何特征，以及一些交互项（即天气和性别、路面状况和驾驶经验、限速和去年里程）显著影响车道保持能力。本研究的一个重要发现表明，雾天条件下受影响的能见度会显著降低车道保持能力。具体来说，在受影响的能见度条件下行驶的驾驶员的车道位置标准差（SDLP）比在不受影响的能见度条件下行驶的驾驶员高 1.37 倍。
这些结果更好地理解了驾驶员的车道保持行为和对雾天条件的感知。此外，这些结果可用于通过允许车道偏离警告（LDW）系统算法考虑雾对能见度的影响来改进 LDW 系统算法。
这些结果更好地理解了驾驶员的车道保持行为和对雾天条件的感知。此外，这些结果可用于通过允许车道偏离警告（LDW）系统算法考虑雾对能见度的影响来改进 LDW 系统算法。
