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-  2018 

驾驶人注意分散的认知模拟与交通流特性
Cognition simulation and traffic flow characteristics analysis in??driving distraction

Keywords: 交通工程,交通安全,驾驶人,注意分散,认知结构,ACT??R,交通流
traffic engineering
,traffic safety,driver,distraction,cognitive structure,ACT??R,traffic flow

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Abstract:

为研究执行车内次任务条件下驾驶人状态变化及其对交通运行造成的影响,首先基于理性思维的自适应控制系统(ACT??R)认知结构与Distract??R软件平台建立了4类次任务情况,分别对各类驾驶人注意分散状态及无次任务影响状态进行认知模拟;获得执行4类不同次任务时的驾驶人操作时间消耗与注意分散时间比数据。并以该数据构建驾驶人注意分散状态库,作为次任务条件下交通流仿真的基础数据。接着,在元胞自动机交通流模型(STCA)的基础上,修正减速规则并建立了考虑车内次任务影响的交通流模型;通过模型数据交换实现了基于元胞自动机模型与认知模型的联合仿真。最后,利用元胞自动机仿真0、10%、20%的驾驶人在行驶中执行车内次任务时的交通流状况,次任务类型在4类次任务中随机抽取。试验结果表明:元胞自动机与认知模型联合仿真能够体现驾驶人心理差异及交通流变化特性;该模型修正了原有的元胞自动机减速规则,且能通过调用驾驶人注意分散状态库获取驾驶人状态参数;车内次任务会对交通流造成明显影响,使道路最大交通流量降低,当10%、20%的驾驶人执行次任务时,最大交通流量分别降低了23.5%、??40.7%??,还造成了拥堵区域增多、排队加剧等现象;执行车内次任务不仅对驾驶安全产生影响,而且当一定比例驾驶人处于注意分散状态时,交通流整体状态也会受到显著影响。
In order to investigate the changes of drivers state and its effects on the traffic flow under the condition of executing vehicle secondary tasks, first of all, based on adaptive control of thought??rational (ACT??R) cognitive structure and Distract??R software platform, four types of secondary tasks were established, and the distraction states of different drivers and the state of non??secondary tasks were simulated cognitively. Obtaining the ratio of the drivers?? execution time and the distracting time during the performance of four types secondary tasks, and the driver distraction state database was established as the basic data for traffic flow simulation under secondary tasks conditions; then, based on the cellular automata traffic flow STCA model, the rules of deceleration were modified and the traffic flow simulation model considering the effects of vehicle secondary task was established. The joint simulation of cellular automata model and cognitive model was realized through model data exchange. Finally, cellular automata simulation was used to simulate the traffic flow situations of 0, 10%, 20% drivers during executing vehicle secondary tasks when they are driving with the four task types randomly selected. Experimental data show that the joint simulation of cellular automata and cognitive model can reflect the psychological difference of drivers and variation characteristics of traffic flow; the model modifies the rules of original cellular automata deceleration, and parts of model parameters can be obtained by calling database of drivers?? distraction states. Additionally, the vehicle secondary task causes obvious effects on the traffic flow to make the road maximize traffic capacity get reduced. The maximize traffic capacity reduced about 23.5% and 40.7% when 10%, 20% drivers executing secondary tasks, with the phenomena of increasing regional congestion and exacerbating queuing. Execution of vehicle secondary task not only causes the impact on the driving safety, but when

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