作者：
Yanli Bao;Xuesong Wang*
通讯作者：
Xuesong Wang
作者机构：
College of Transportation Engineering, Tongji University, China
The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai, China
National Engineering Laboratory for Integrated Optimization of Road Traffic and Safety Analysis Technologies, China
通讯机构：
College of Transportation Engineering, Tongji University, China
The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai, China
National Engineering Laboratory for Integrated Optimization of Road Traffic and Safety Analysis Technologies, China
语种：
英文
关键词：
Truck forward collision warning,Algorithm optimization,Active safety system data,Response behavior,Long short-term memory
期刊：
Accident analysis and prevention
ISSN：
0001-4575
年：
2024
卷：
198
页码：
107450
基金类别：
The data used in this study were collected by CPIC’s 10 insured trucks. ADAS, which includes FCW, was installed in the trucks and was used to collect data for 2020. The truck FCW system was provided by the Chinese supplier Roadefend, and consists of digital video recorders (DVRs), GPS, and an on-board unit (OBU). The physical appearance of the truck FCW system is shown in Fig. 1, along with a typical warning display. The system’s forward camera is responsible for sensing roadway information
摘要：
Forward collision warning (FCW) systems have been widely used in trucks to alert drivers of potential road situations so they can reduce the risk of crashes. Research on FCW use shows, however, that there are differences in drivers’ responses to FCW alerts under different scenarios. Existing FCW algorithms do not take differences in driver response behavior into account, with the consequence that the algorithms’ minimum safe distance assessments that trigger the warnings are not always appropriate for every driver or situation. To reduce false alarms, this study analyzed truck driver behavior in response to FCW warnings, and k-means clustering was adopted to classify dr...
