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MODELING STEERING USING THE QUEUEING NETWORK - MODEL HUMAN PROCESSOR (QN-MHP)

机译:使用排队网络进行建模-人机模型(QN-MHP)

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The Queueing Network - Model Human Processor (QN-MHP) is a computational architecture that combines the mathematical theories and simulation methods of queueing networks (QN) with the symbolic and procedure methods of a GOMS-style task description and the Model Human Processor (MHP). Using QN-MHP, a steering model was created to represent the concurrent perceptual, cognitive, and motor activities involved in vehicle steering as truly concurrent processes. The model was compared with driving performance of human subjects and demonstrated realistic steering behavior. It steered a simulated vehicle at a fixed speed within the lane boundaries of straight sections and curves of different radii. In a quantitative validation of several basic measures of driving performance, the steering model yielded steering angle and lateral position similar to the human subject data. This work showed the strength of QN- MHP as a model of driving behavior. Ongoing work further develops the model by expanding the scope of the driving task and by adding a concurrent secondary in-vehicle task.
机译:排队网络-模型人处理器(QN-MHP)是一种计算体系结构,结合了排队网络(QN)的数学理论和仿真方法以及GOMS式任务描述和模型人处理器(MHP)的符号和过程方法)。使用QN-MHP,创建了一个转向模型,将车辆转向中涉及的并发感性,认知和运动活动表示为真正的并发过程。该模型与人类受试者的驾驶性能进行了比较,并证明了逼真的转向行为。它在直线段和不同半径的曲线的车道边界内以固定速度操纵模拟车辆。在对驾驶性能的几种基本度量进行定量验证时,转向模型得出的转向角和侧向位置与人类受试者数据相似。这项工作表明了QN-MHP作为驾驶行为模型的力量。正在进行的工作通过扩展驾驶任务的范围并添加并发的辅助车载任务来进一步开发模型。

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