Driving control in the car-following (CF) driving behavior has two aspects. First, in what measure an approximation distance is taken as a safe distance guaranteeing the safety of the follower drivers. Second, how to control the follower's vehicle velocities based on the stimulus of the leading vehicle. In this context, to resolve the driving control problem in the CF driving behavior, a bilevel optimization is presented in this paper, based on the behaviors of the follower and leader drivers. Bearing in mind that mathematics has contributed to the imitation of human behaviors, they are now reaching a level of complexity requiring the entry on the scene of a new player, which is artificial intelligence. Thus, in this paper; we used the fuzzy logic theory for modeling a follower driver with a nonnormative behavior. To validate our model, we used a data set from the program of the US Federal Highway Administration. Therefore, according to the experimental results, there is homogeneity between the actual and the simulated travel trajectories in terms of deviation. Besides, the driver's behavior adopted (normative or nonnormative) is reflected in his reactions to the various components of the road.

中文翻译：

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基于双层优化和模糊逻辑的驾驶控制

跟车（CF）驾驶行为中的驾驶控制有两个方面。首先，在什么措施下近似距离被作为保证跟随司机安全的安全距离。其次，如何根据前车的刺激来控制跟车的车速。在此背景下，为了解决 CF 驾驶行为中的驾驶控制问题，本文提出了一种基于跟随者和领导者驾驶员行为的双层优化。请记住，数学有助于模仿人类行为，它们现在达到了需要新玩家进入场景的复杂程度，这就是人工智能。因此，在本文中；我们使用模糊逻辑理论对具有非规范行为的跟随驱动器进行建模。为了验证我们的模型，我们使用了美国联邦公路管理局项目中的数据集。因此，根据实验结果，在偏差方面，实际和模拟的行驶轨迹之间存在同质性。此外，驾驶员所采用的行为（规范性或非规范性）反映在他对道路各个组成部分的反应中。