Modeling Car-Following Behavior on Freeways Considering Driving Style

2021 ◽  
Vol 147 (12) ◽  
pp. 04021083
Author(s):  
Ping Sun ◽  
Xuesong Wang ◽  
Meixin Zhu
Keyword(s):  
Author(s):  
Motonori Ishibashi ◽  
Masayuki Okuwa ◽  
Shun'ichi Doi ◽  
Motoyuki Akamatsu
Keyword(s):  

2017 ◽  
Vol 44 (10) ◽  
pp. 775-782
Author(s):  
Fei Tan ◽  
Da Wei ◽  
Jianqi Zhu ◽  
Dong Xu ◽  
Kexin Yin

The complexity of the driving behavior restricts the realism of traffic simulation. This paper proposed that vehicle mobility models should be established according to diverse driving styles to further approximation of real driving behavior. With Krauss model represented, the conservative (driving style) of safe distance car-following model is analyzed. The analysis means that real vehicles can occasionally break the safe distance rule, and on average, real vehicle gap is slightly smaller than that in the Krauss model. An aggressive car-following model is proposed in the view of driving style. Simulation results show the new model can simulate aggressive driving style, which has significance to simulate traffic using diverse driving style models. Since it breaks the safe distance rule, the new model has the possibility of generating rear-end collisions when simulating. Drivers’ characteristics, prediction behavior, the cause of accidents, and the effects of time granularity on a simulation are studied. The concept of “road black hole” is put forward, which is believed to reduce velocity of traffic flow.


Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4011 ◽  
Author(s):  
Óscar Mata-Carballeira ◽  
Jon Gutiérrez-Zaballa ◽  
Inés del Campo ◽  
Victoria Martínez

Advanced driving-assistance systems (ADAS) are intended to automatize driver tasks, as well as improve driving and vehicle safety. This work proposes an intelligent neuro-fuzzy sensor for driving style (DS) recognition, suitable for ADAS enhancement. The development of the driving style intelligent sensor uses naturalistic driving data from the SHRP2 study, which includes data from a CAN bus, inertial measurement unit, and front radar. The system has been successfully implemented using a field-programmable gate array (FPGA) device of the Xilinx Zynq programmable system-on-chip (PSoC). It can mimic the typical timing parameters of a group of drivers as well as tune these typical parameters to model individual DSs. The neuro-fuzzy intelligent sensor provides high-speed real-time active ADAS implementation and is able to personalize its behavior into safe margins without driver intervention. In particular, the personalization procedure of the time headway (THW) parameter for an ACC in steady car following was developed, achieving a performance of 0.53 microseconds. This performance fulfilled the requirements of cutting-edge active ADAS specifications.


2003 ◽  
Author(s):  
Nicholas J. Ward ◽  
Michael P. Manser ◽  
Dick de Waard ◽  
Nobuyuki Kuge ◽  
Erwin Boer

2018 ◽  
Vol 1 (1) ◽  
pp. 39-42
Author(s):  
Laszlo Barothi ◽  
◽  
Daniel Sava ◽  
Cătălin-Dumitru Darie ◽  
Leonard-Iulian Cucu ◽  
...  
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