基于扩展计划行为理论的驾驶员跟驰意向分析

Analysis of driver′s car-following intention based on extended planning behavior theory

未来道路交通流将呈现自动驾驶车辆和传统车辆混行的现象,为探究人机混驾环境下传统车辆驾驶员对自动驾驶车辆跟驰意向的影响因素,引入驾驶员对自动驾驶车辆的了解程度、风险感知及接受程度3个变量,构建基于扩展计划行为理论的驾驶员跟驰意向模型框架.通过问卷调查获取331份主观评价数据,并借助SPSS和AMOS软件检验数据的内部一致性及可靠性.运用结构方程模型进行路径分析及中介效应分析以检验影响因素间的关系.结果表明,基于扩展计划行为理论的驾驶员跟驰意向结构方程模型对人机混驾环境下驾驶员的跟驰意向具有良好解释力;行为态度、主观规范和知觉行为控制对驾驶员跟驰意向具有显著正向直接效应;风险感知和接受程度通过中介变量对驾驶员跟驰意向产生显著间接效应,其中,风险感知作用为负向,接受程度作用为正向;了解程度对驾驶员跟驰意向既有显著正向直接效应又有显著正向间接效应.研究结果可作为人机混驾环境下车辆交互行为分析的基础.

Road traffic flow will show the phenomenon of mixed driving of autonomous vehicles and traditional vehicles in the future. In order to explore psychological factors that affect traditional vehicle drivers’ intention to follow autonomous vehicles in the human-machine hybrid driving environment, three variables of driver’s understanding, risk perception, and acceptance of autonomous vehicles are introduced to construct an extended theoretical framework of planning behavior. Three hundred thirty-one subjective evaluation data are obtained through questionnaire survey. The internal consistency and reliability of the data are tested by SPSS and AMOS softwares. Finally, the path analysis and mediation effect analysis are carried out by using structural equation model to test relationship among the influencing factors. The results show that the structural equation model of driver’s carfollowing intention based on the theory of extended planning behavior has good explanatory power for the driver’s carfollowing intention in the mixed driving environment. Driver’s behavioral attitude, subjective norm and perceived behavioral control of autonomous vehicles have a significant positive and direct effect on driver’s car-following intention. Driver’s risk perception and acceptance of autonomous vehicles have a significant indirect effect on driver’s car-following intention through intermediary variables, in which the effect of risk perception is negative, and the effect of acceptance is positive. The degree of understanding has both a significant positive direct effect and a significant positive indirect effect on driver’s car-following intention. The research results can be used as the basis for vehicle interaction behavior analysis in the human-machine hybrid driving environment.