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换道过程中驾驶人感知操作的模式发现与规则挖掘
引用本文:龙彦,黄建玲,赵晓华.换道过程中驾驶人感知操作的模式发现与规则挖掘[J].交通运输系统工程与信息,2021,21(3):237-246.
作者姓名:龙彦  黄建玲  赵晓华
作者单位:1. 北京工业大学,城市交通学院,交通工程北京市重点实验室,北京 100124;2. 北京市交通信息中心,北京 100073
基金项目:国家自然科学基金/National Natural Science Foundation of China (61876011)。
摘    要:为发现高速公路下自由换道过程中眼睛感知-手脚操作之间的时序关联性,探索感知与操作相互作用的内在机理,采用驾驶模拟舱进行高速公路驾驶实验,采集眼动数据和车辆运行数据;分别提取换道瞬时和换道全过程的眼睛感知-手脚操作的特征;采用Aprior算法从换道瞬时和换道全过程两个角度发现眼睛感知-手脚操作的频繁模式,挖掘它们的关联规则。对于瞬时感知-操作,左换道发现13种频繁3项集模式,右换道发现18种频繁3项集模式;对于全过程感知- 操作,左换道发现4种频繁模式,右换道发现3种频繁模式。左右换道各自挖掘到6条有实际价值的关联规则。对频繁模式和规则分析发现:右换道比左换道需要较多的感知时间、较复杂的手脚操作行为。发现的频繁模式和挖掘的关联规则描述了自由换道过程中感知操作的特征和它们之间的关联性,能够为安全换道提供参考,为无人驾驶换道操作提供支撑。

关 键 词:交通工程  关联规则  Aprior算法  换道行为  模式发现  
收稿时间:2021-03-08

Pattern Discovery and Rule Mining of Drivers' Perception and Operation During Lane Changing Process
LONG Yan,HUANG Jian-ling,ZHAO Xiao-hua.Pattern Discovery and Rule Mining of Drivers' Perception and Operation During Lane Changing Process[J].Transportation Systems Engineering and Information,2021,21(3):237-246.
Authors:LONG Yan  HUANG Jian-ling  ZHAO Xiao-hua
Institution:1. Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China; 2. Beijing Transportation Information Center, Beijing 100073, China
Abstract:To find the correlation between eye perceiving and hand- foot operating during the process of discretionary lane changing on the highway, and to explore the mechanism of the interaction between perception and operation, a driving simulator was used to carry out a highway driving experiment. Eye movement data and vehicle operation data were collected. Features of eye perceiving and hand-foot operating for an instant and during the whole process of lane changing were extracted. The Aprior algorithm was used to find frequent patterns and mine association rules of eye perceiving and hand- foot operating. For the instantaneous perception- operation, 13 frequent 3-itemset patterns were found in left lane changing and 18 frequent 3- itemset patterns were found in right lane changing. For the whole process, 4 frequent patterns were found in left lane changing and 3 frequent patterns were found in right lane changing. Six valuable association rules were found in the left lane and the right lane, respectively. Through the analysis of frequent patterns and association rules, right lane changing needs more perception time and more complicated handfoot operating behavior than left lane changing. The frequent patterns and association rules describe the characteristics of perception- operation and the association between them in the process of discretionary lane changing, which can provide the reference for safe lane changing and support for lane changing operations of unmanned vehicles.
Keywords:traffic engineering  association rules  Aprior algorithm  lane changing behavior  pattern discovery  
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