Investigation of Bus Drivers’ Reaction to ADAS Warning System: Application of the Gaussian Mixed Model

Wei Ye, Yueru Xu, Feixiang Zhou, Xiaomeng Shi and Zhirui Ye
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Wei Ye: Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 211189, China
Yueru Xu: Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 211189, China
Feixiang Zhou: Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 211189, China
Xiaomeng Shi: Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 211189, China
Zhirui Ye: Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 211189, China

Sustainability, 2021, vol. 13, issue 16, 1-19

Abstract: Road crashes cause serious loss of life and property. Among all vehicles, buses are more likely to encounter crashes. In recent years, the advanced driving assistance system (ADAS) has been widely used in buses to improve safety. The warning system is one of the key functions and has proven effective in reducing crashes. However, drivers often ignore or overreact to ADAS warnings during naturalistic driving scenarios. Therefore, reactions of bus drivers to warnings need further investigation. In this study, bus drivers’ responses to lane departure warning (LDW) and forward collision warning (FCW) were investigated using 20-day naturalistic driving data. These reactions could be classified into three categories, namely positive, negative, and overreaction or emergency, by employing the Gaussian mixture model. The authors constructed a framework to quantify drivers’ reactions to the warning and study the reaction characteristics in different environments. The results indicate that drivers’ reactions to FCW were more positive than to LDW, drivers reacted more positively to LDW and FCW while driving on highways than on urban roads, and drivers reacted more positively at night to LDW and FCW than during daytime. This study gives support to an adaptive ADAS considering varying bus driver characteristics and environments.

Keywords: advanced driver assistance systems; forward collision warning; lane departure warning; bus drivers’ reaction; Gaussian mixed model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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