Abstract
PurposeAdvanced driving assistance system (ADAS) has been applied in commercial vehicles. This paper aims to evaluate the influence factors of commercial vehicle drivers’ acceptance on ADAS and explore the characteristics of each key factors. Two most widely used functions, forward collision warning (FCW) and lane departure warning (LDW), were considered in this paper.Design/methodology/approachA random forests algorithm was applied to evaluate the influence factors of commercial drivers’ acceptance. ADAS data of 24 commercial vehicles were recorded from 1 November to 21 December 2018, in Jiangsu province. Respond or not was set as dependent variables, while six influence factors were considered.FindingsThe acceptance rate for FCW and LDW systems was 69.52% and 38.76%, respectively. The accuracy of random forests model for FCW and LDW systems is 0.816 and 0.820, respectively. For FCW system, vehicle speed, duration time and warning hour are three key factors. Drivers prefer to respond in a short duration during daytime and low vehicle speed. While for LDW system, duration time, vehicle speed and driver age are three key factors. Older drivers have higher respond probability under higher vehicle speed, and the respond time is longer than FCW system.Originality/valueFew research studies have focused on the attitudes of commercial vehicle drivers, though commercial vehicle accidents were proved to be more severe than passenger vehicles. The results of this study can help researchers to better understand the behavior of commercial vehicle drivers and make corresponding recommendations for ADAS of commercial vehicles.
Highlights
Transportation system has caused a lot of concern in many aspects including efficiency (Gao et al.,2020; Wu et al, 2020; Li et al, 2019), environmental protection (Gao et al, 2021; Xu et al, 2021; Qu et al, 2020) and safety (Meng and Qu, 2012; Kuang et al, 2015; Shi et al 2021)
Python software was used in this study to develop the random forests algorithm and calculate the out of bag (OOB) error and importance. Both forward collision warning (FCW) and lane departure warning (LDW) records and relative variables were applied to the random forests
Six significant variables were used in random forests for FCW and LDW records, separately
Summary
Transportation system has caused a lot of concern in many aspects including efficiency (Gao et al.,2020; Wu et al, 2020; Li et al, 2019), environmental protection (Gao et al, 2021; Xu et al, 2021; Qu et al, 2020) and safety (Meng and Qu, 2012; Kuang et al, 2015; Shi et al 2021). Due to the serious consequences of road accidents on property and human life, road safety has been widely studied by relevant researchers for decades. Road accidents is expected to increase from the ninth factor of mortality to the seventh by 2030, which cause approximately 1.8 million deaths per year (World Health Organization, 2018). Approximately 90% of road crashes are related to human factors (Treat et al, 1979). Various efforts have been made to better understand drivers’ driving behavior to improve road safety
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