摘要
针对道路交通系统数据采集难度大、灰度大、无典型分布等特点,采用多种数据采集设备采获数据,运用灰关联熵分析法重点对来自道路、环境的驾驶决策主影响因子进行了榨取和排序。仿真结果表明,该方法客观、定量,能克服目前人们以主观、定性分析为主的缺点,避免了多因子多重共线关系所引起的信息重叠及其对仿真过程的干扰,为自动驾驶系统的仿真和实现提供了理论基础和可行性依据。
Aiming at the great difficulties, big gray scale and non-typical distribution of data in road traffic data collection, a variety of data acquisition equipment is used to collect data. Gray relation entropy analysis is adopted to extract and sort the principal factors from road and environment. Simulation results show that this method is so objective and quantitative that the weakness of subjective analysis can be avoided effectively. At the same time, information overlapping and its disturbance caused by multi-factors to the simulation process can be avoided. This study provides a theoretical basis for the simulation and realization of automatic driving system.
出处
《中国安全科学学报》
CAS
CSCD
2007年第5期126-132,共7页
China Safety Science Journal
基金
山东省自然科学基金资助(Y2006G32)
山东省社会科学规划研究项目(04CMZ08)
山东理工大学科研重点资助项目(2004KJZ02)。
关键词
驾驶员行为
驾驶决策
主影响因子
灰关联熵分析法
交通流
智能运输系统(ITS)
driving behavior
driving decision-making
principal impact factor
grey relation entropy analysis method
traffic flow
intelligent transportation system（ITS）
作者简介
教授
