Multi-agent and driving behavior based rear-end collision alarm modeling and simulating

被引：9

作者：

Jun, Liang ^{[1
]}

Long, Chen ^{[2
]}

Xian-yi, Cheng ^{[1
]}

Xian-bo, Chen ^{[1
]}

机构：

[1] JiangSu Univ, Sch Comp Sci & Telecommun Engn, Zhenjiang 212013, Peoples R China

[2] JiangSu Univ, Sch Automobile & Traff Engn, Zhenjiang 212013, Peoples R China

来源：

SIMULATION MODELLING PRACTICE AND THEORY | 2010年 / 18卷 / 08期

关键词：

Multi-agent; Rear-end warning system; Simulation;

D O I：

10.1016/j.simpat.2010.02.006

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Distance-between-vehicle-measurement is the only factor in traditional car rear-end alarm system. To address the above problem, this paper proposes an alarming model based on multi-agent systems (MAS) and driving behavior. It consists of four different types of agents that can either work alone or collaborate through a communications protocol on the basis of the extended KQML The rear-end alarming algorithm applies the Bayes decision theory to calculate the probability of collision and prevent its occurrence real-time. The learning algorithm of driving behavior based on ensemble artificial neural network (ANN) and the decision procedure based on Bayes' theory are also described in this paper. Both autonomy and reliability are enhanced in the proposed system. The effectiveness and robustness of the model have been confirmed by the simulated experiments. Crown Copyright (C) 2010 Published by Elsevier B.V. All rights reserved.

引用

页码：1092 / 1103

页数：12