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首页> 外文期刊>Intelligent Transport Systems, IET >Predicting drivers' direction sign reading reaction time using an integrated cognitive architecture
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Predicting drivers' direction sign reading reaction time using an integrated cognitive architecture

机译：预测司机的方向标志阅读反应时间使用综合认知架构

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摘要

Drivers' reaction time of reading signs on expressways is a fundamental component of sight distance design requirements, and reaction time is affected by many factors such as information volume and concurrent tasks. We built cognitive simulation models to predict drivers' direction sign reading reaction time. Models were built using the queueing network-adaptive control of thought rational (QN-ACTR) cognitive architecture. Drivers' task-specific knowledge and skills were programmed as production rules. Two assumptions about drivers' strategies were proposed and tested. The models were connected to a driving simulator program to produce prediction of reaction time. Model results were compared to human results in sign reading single-task and reading while driving dual-task conditions. The models were built using existing modelling methods without adjusting any parameter to fit the human data. The models' prediction was similar to the human data and could capture the different reaction time in different task conditions with different numbers of road names on the direction signs. Root mean square error (RMSE) was 0.3 s, and mean absolute percentage error (MAPE) was 12%. The results demonstrated the models' predictive power. The models provide a useful tool for the prediction of driver performance and the evaluation of direction sign design.
机译：在高速公路上阅读迹象的司机反应时间是视距设计要求的基本组件，并且反应时间受到信息量和并发任务等许多因素的影响。我们建立了认知模拟模型，以预测驱动器的方向标志读取反应时间。模型是利用思想Rational（QN-Actr）认知架构的排队网络自适应控制。驱动程序的任务特定知识和技能被编程为生产规则。提出并测试了关于司机策略的两个假设。该模型连接到驾驶模拟器程序以产生反应时间的预测。将模型结果与人类导致进行比较，在驾驶双任务条件的同时阅读单次任务和读数。使用现有的建模方法构建模型，无需调整任何参数以适合人类数据。模型的预测类似于人类数据，并且可以在不同任务条件下捕获不同的反应时间，在方向标志上具有不同数量的道路名称。根均方误差（RMSE）为0.3秒，平均绝对百分比误差（MAPE）为12％。结果表明了模型的预测力。该模型为预测驾驶员性能和方向标志设计的评估提供了一个有用的工具。

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