Dynamic changes of traffic features in unstructured road networks challenge the scene-cognitive abilities of drivers, which brings various heterogeneous traffic behaviors. Modeling traffic with these heterogeneous behaviors would have significant impact on realistic traffic simulation. Most existing traffic methods generate traffic behaviors by adjusting parameters and cannot describe those heterogeneous traffic flows in detail. In this paper, a cognition-driven trafficsimulation method inspired by the theory of cognitive psychology is introduced. We first present a visual-filtering model and a perceptual-information fusion model to describe drivers’ heterogeneous cognitive processes. Then, logistic regression is used to model drivers’ heuristic decision-making processes based on the above cognitive results. Lastly, we apply the high-level cognitive decision-making results to low-level traffic simulation. The experimental results show that our method can provide realistic simulations for the traffic with those heterogeneous behaviors in unstructured road networks and has nearly the same efficiency as that of existing methods.

中文翻译：

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非结构化道路网络的认知驱动交通仿真

非结构化道路网络中交通特征的动态变化对驾驶员的场景认知能力提出了挑战，带来了各种异构的交通行为。使用这些异构行为对交通进行建模将对现实交通模拟产生重大影响。现有的交通方式大多通过调整参数来产生交通行为，无法详细描述那些异构的交通流。本文介绍了一种受认知心理学理论启发的认知驱动交通仿真方法。我们首先提出了一个视觉过滤模型和一个感知信息融合模型来描述驾驶员的异构认知过程。然后，基于上述认知结果，使用逻辑回归对驾驶员的启发式决策过程进行建模。最后，我们将高级认知决策结果应用于低级交通模拟。实验结果表明，我们的方法可以为非结构化道路网络中具有异构行为的交通提供逼真的模拟，并且具有与现有方法几乎相同的效率。