Modeling Commercial Vehicle Drivers’ Acceptance of Forward Collision Warning System
Paper i proceeding, 2021

With the development of computer science, Forward Collision Warning (FCW) systems have been installed in various vehicles in order to improve road safety. Previous studies have been conducted to evaluate the acceptance of FCW systems and explore the contributing factors affecting drivers’ attitudes. However, few research studies have focused on the attitudes of commercial vehicle drivers, though commercial vehicle accidents were proved to be more severe than passenger vehicles. This paper tries to examine the attitudes of commercial vehicle drivers toward FCW systems and identify the contributing factors by using a random forests algorithm. FCW data of 24 commercial vehicles were recorded from November 1st to December 21st, 2018 in Jiangsu province. The acceptance rate (FCW records with response) of commercial vehicle drivers for FCW systems is 69.52%. (Acceptance was measured by identifying drivers who reduced their speed in response to a warning from the FCW system.) The accuracy of random forests model is 0.816 after tuning the parameter. In addition, the most important influence variable in this model is vehicle speed with an importance of 0.37. Duration time and warning hour also have significant influence on driver reaction, with values of 0.20 and 0.17, respectively. The results showed that commercial vehicle drivers’ acceptance of an FCW system decreases with the increase of vehicle speed. The response time for most cases is timely, usually within 2 s. And the response percentage is higher during daytime than at night. These regularities may be attributable to the larger size and heavier weight of commercial vehicles. The results of this study can help researchers to better understand the behavior of commercial vehicle drivers and to develop more effective FCW systems for commercial vehicles.

Driving behavior

Commercial vehicles

Random forests

Forward collision warning system


Yueru Xu

Southeast University

Zhirui Ye

Southeast University

Chao Wang

Southeast University

Kun Gao

Chalmers, Arkitektur och samhällsbyggnadsteknik, Geologi och geoteknik

Smart Innovation, Systems and Technologies

2190-3018 (ISSN) 2190-3026 (eISSN)

Vol. 231 167-180
9789811623233 (ISBN)

4th International Symposium on Smart Transportation Systems, KES-STS 2021
Virtual, Online, ,




Transportteknik och logistik





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