Effect Evaluation of Forward Collision Warning System Using IoT Log and Virtual Driving Simulation Data

Abstract

Advanced driver-assistance systems (ADAS) are primarily known for their positive impact in improving the safety of drivers. Previous studies primarily analyzed the positive effects of ADAS with short-term experiments and accident data without considering the long-term changes in drivers’ safety perception. The human factor is the most dominant among factors that cause traffic accidents, and safety effect evaluation should be performed considering changes in human errors. To this end, this study classified the safety effect of ADAS-forward collision warning (FCW) on taxi drivers in Seoul into behavioral control and attitude change to perform analysis on respective factors. With regard to behavioral control, virtual driving simulation was used to analyze the reaction time of drivers and deceleration rate, and for attitude change, autoregressive integrated moving average (ARIMA) time series analysis was employed to predict the long-term perception change of drivers. The analysis results indicated that, in terms of behavioral control, ADAS-FCW reduces the cognitive reaction time of drivers in risk situations on the road, similar to the findings in previous studies. However, in terms of attitude change, ADAS-FCW has the adverse long-term effect of increasing violations in maintaining safety distance in the case of nighttime-drivers under 60 years old. As can be seen from these results, new technologies in the road safety arena can have a short-term effect of improving safety with behavioral control but may have a negative impact in the long term. The results of this study are expected to provide a theoretical basis for reference in the safety evaluation of ADAS and traffic safety facilities.

Keywords

• ADAS
• IoT platform
• dangerous driving behaviors
• autoregressive integrated moving average (ARIMA)
• safety effect estimation