Journal of South China University of Technology (Natural Science Edition) ›› 2016, Vol. 44 ›› Issue (12): 74-80.doi: 10.3969/j.issn.1000-565X.2016.12.011

• Traffic & Transportation Engineering • Previous Articles     Next Articles

A Neural Network-Based Autonomous Articulated Vehicle System Considering Driver Behavior

ZHANG Wen-ming HAN Hong-bing YANG Jue YI Xiao   

  1. School of Mechanical Engineering,University of Science and Technology Beijing,Beijing 100083,China
  • Received:2016-05-12 Revised:2016-08-09 Online:2016-12-25 Published:2016-11-01
  • Contact: 杨珏(1975-),男,博士,副教授,主要从事非公路车辆设计研究. E-mail:yangjue@ustb.edu.cn
  • About author:张文明(1955-),男,博士,教授,主要从事非公路车辆设计、非公路车辆状态检测与故障诊断研究. E-mail:wmzhang@ ustb. edu. cn
  • Supported by:

    Supported by the National High-Tech R&D Program of China(863 Program)(2011AA060404)

Abstract:

In view of the steering characteristics of articulated dump trucks,an autonomous articulated vehicle sys- tem is proposed based on neural networks and by considering driver behaviors.First,a sensor collecting system based on the laser radar and the angular transducer is established,and an articulated vehicle kinematics model and a dynamics model of articulated dump trucks are constructed by analyzing the steering characteristics of articulated dump trucks.Then,by using the ADAMS software,a dynamic model of the trucks is constructed to perform a steady state test.Moreover,a driver model of the artificial neural network control algorithm is constructed based on the optimal preview control,and it is verified by an Adams-Matlab/Simulink co-simulation.Finally,this control model is also verified by establishing a simulation ground tunnel to perform the straight-road-return and curve-road- following tests.The results show that,when the constructed control model is applied to the variable curvature road,the lateral position error is less than 10% of the passable distance,and 90% of the course angle deviation is opti- mized,which indicates that the constructed control model has a high convergence speed,a good steady state and an excellent unmanned driving performance,

Key words: driver model, neural networks, dynamic model, co-simulation

CLC Number: