An improved driver-behavior model with combined individual and general driving characteristics

2012 ◽  
Author(s):  
Pongtep Angkititrakul ◽  
Chiyomi Miyajima ◽  
Kazuya Takeda
Keyword(s):  
Driver Behavior ◽  
Behavior Model

scholarly journals Driver Behavior Model in Emergency Situations. On Basic Structure of A Driver Model.

1999 ◽  
Vol 65 (633) ◽  
pp. 1966-1972 ◽  
Author(s):  
Yasushi AMANO ◽  
Sueharu NAGIRI ◽  
Masatoshi HADA ◽  
Shun-ichi DOI
Keyword(s):  
Driver Behavior ◽  
Basic Structure ◽  
Driver Model ◽  
Behavior Model ◽  
Emergency Situations

Estimation of Driver Behavior Model Parameters

1990 ◽  
Vol 116 (2) ◽  
pp. 153-166 ◽  
Author(s):  
John C. Golias ◽  
George C. Kanellaidis
Keyword(s):  
Driver Behavior ◽  
Model Parameters ◽  
Behavior Model

scholarly journals Development of Driver-Behavior Model Based onWOA-RBM Deep Learning Network

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Junhui Liu ◽  
Yajuan Jia ◽  
Yaya Wang ◽  
Petr Dolezel
Keyword(s):  
Deep Learning ◽  
Optimization Algorithm ◽  
Driver Behavior ◽  
Restricted Boltzmann Machine ◽  
Whale Optimization Algorithm ◽  
Behavior Model ◽  
Boltzmann Machine ◽  
Learning Network ◽  
Whale Optimization ◽  
Deep Learning Network

Human drivers’ behavior, which is very difficult to model, is a very complicated stochastic system. To characterize a high-accuracy driver behavior model under different roadway geometries, the paper proposes a new algorithm of driver behavior model based on the whale optimization algorithm-restricted Boltzmann machine (WOA-RBM) method. This method establishes an objective optimization function first, which contains the training of RBM deep learning network based on the real driver behavior data. Second, the optimal training parameters of the restricted Boltzmann machine (RBM) can be obtained through the whale optimization algorithm. Finally, the well-trained model can be used to represent the human drivers’ operation effectively. The MATLAB simulation results showed that the driver model can achieve an accuracy of 90%.

scholarly journals Report on layout of the traffic simulation and trial design of the evaluation

2021 ◽  
Author(s):  
Christian Siebke ◽  
◽  
Maximilian Bäumler ◽  
Madlen Ringhand ◽  
Marcus Mai ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Trial Design ◽  
Traffic Simulation ◽  
Driver Behavior ◽  
Flow Simulation ◽  
Behavior Model ◽  
Behavior Study ◽  
Safety Function ◽  
Traffic Flow Simulation

Within the AutoDrive project, openPASS is used to develop a cognitive stochastic traffic flow simulation for urban intersections and highway scenarios, which are described in deliverable D1.14. The deliverable D2.16 includes the customizations of the framework openPASS that are required to provide a basis for the development and implementation of the driver behavior model and the evaluated safety function. The trial design for the evaluation of the safety functions is described. Furthermore, the design of the driver behavior study is introduced to parameterize and validate the underlying driver behavior model.

Sign in / Sign up

Export Citation Format

Share Document