Analyzing the effect of fog weather conditions on driver lane-keeping performance using the SHRP2 naturalistic driving study data.

INTRODUCTION

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.

METHOD

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.

RESULTS

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.

CONCLUSIONS

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.