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A computational model for drivers cognitive state visual perception and intermittent attention in a distracted car following task

机译:分散注意力的汽车跟随任务时驾驶员的认知状态视觉感知和间歇性注意力的计算模型

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摘要

We present a computational model of intermittent visual sampling and locomotor control in a simple yet representative task of a car driver following another vehicle. The model has a number of features that take it beyond the current state of the art in modelling natural tasks, and driving in particular. First, unlike most control theoretical models in vision science and engineering—where control is directly based on observable (optical) variables—actions are based on a temporally enduring internal representation. Second, unlike the more sophisticated engineering driver models based on internal representations, our model explicitly aims to be psychologically plausible, in particular in modelling perceptual processes and their limitations. Third, unlike most psychological models, it is implemented as an actual simulation model capable of full task performance (visual sampling and longitudinal control). The model is developed and validated using a dataset from a simplified car-following experiment (N = 40, in both three-dimensional virtual reality and a real instrumented vehicle). The results replicate our previously reported connection between time headway and visual attention. The model reproduces this connection and predicts that it emerges from control of action uncertainty. Implications for traffic psychological models and future developments for psychologically plausible yet computationally rigorous models of full natural task performance are discussed.
机译:我们提出了一个简单但具有代表性的汽车驾驶员跟随另一辆车的任务时,间歇性视觉采样和运动控制的计算模型。该模型具有许多功能,可以在对自然任务进行建模(尤其是驾驶)方面超越当前的最新水平。首先,不同于视觉科学和工程学中的大多数控制理论模型(控制直接基于可观察的(光学)变量),动作基于时间上持久的内部表示。其次,与基于内部表示的更复杂的工程驱动器模型不同,我们的模型明确旨在在心理上合理,尤其是在建模感知过程及其局限性方面。第三,与大多数心理模型不同,它被实现为能够完成全部任务性能(视觉采样和纵向控制)的实际仿真模型。该模型是使用简化的汽车跟踪实验(在三维虚拟现实和实际仪表车中均为N = 40)中的数据集开发和验证的。结果复制了我们先前报道的时间进展与视觉注意力之间的联系。该模型重现了这种联系,并预测它是通过控制动作不确定性而出现的。讨论了交通心理模型的含义以及完全合理的自然任务执行的心理上合理但计算严格的模型的未来发展。

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