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

2021 ◽  
Vol 13 (16) ◽  
pp. 8759
Author(s):  
Wei Ye ◽  
Yueru Xu ◽  
Feixiang Zhou ◽  
Xiaomeng Shi ◽  
Zhirui Ye

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.

Author(s):  
Yassin Kortli ◽  
Mehrez Marzougui ◽  
Mohamed Atri

In recent years, in order to minimize traffic accidents, developing driving assistance systems for security has attracted much attention. Lane detection is an essential element of avoiding accidents and enhancing driving security. In this chapter, the authors implement a novel real-time lighting-invariant lane departure warning system. The proposed methodology works well in different lighting conditions, such as in poor conditions. The experimental results and accuracy evaluation indicates the efficiency of the system proposed for lane detection. The correct detection rate averages 97% and exceeds 95.6% in poor conditions. Furthermore, the entire process has only 29 ms per frame.


2020 ◽  
Vol 38 (2) ◽  
pp. 1519-1530 ◽  
Author(s):  
Chang Wang ◽  
Qinyu Sun ◽  
Zhen Li ◽  
Hongjia Zhang ◽  
Rui Fu

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