In this paper, a novel driver model for lane keeping is proposed to replicate the steering behavior of expert drivers. Specifically, a feedforward-feedback control scheme mocking expert drivers is adopted: the feedforward controller plays a leading role, which is a data-driven model based on deep convolutional fuzzy systems (DCFS), and for the sake of human-simulation and guaranteed stability, a supervisory feedback controller is designed, which works only if the state hits the set boundary. Comparing with the previous driver models, the key novelty of the paper is to introduce the “motor intermittency” of human behavior into driver modeling, which is an important issue for biological modeling of the drivers. Simulations on the joint platform of PreScan and CarSim show that the newly presented driver model has better matching performance to the expert drivers comparing with the two different types of advanced model predictive control (MPC) controllers. The proposed driver model has the potential application for semi-automated vehicles to provide human-like qualities for automated driving, which may be one of the essential points to promote the comfort when the driver hands over the steering authority, and improve the transition smoothness in the scenario of human vehicle co-piloting.

在本文中，提出了一种新的车道保持驾驶员模型来复制专家驾驶员的转向行为。具体来说，采用模拟专家驱动程序的前馈-反馈控制方案：前馈控制器起主导作用，它是一种基于深度卷积模糊系统（DCFS）的数据驱动模型，并且为了人工模拟和保证稳定性，设计了一个监督反馈控制器，该控制器仅在状态达到设置的边界时才起作用。与以前的驾驶员模型相比，本文的关键新颖之处在于将人类行为的“运动间歇性”引入驾驶员模型中，这是驾驶员生物学模型的重要问题。在PreScan和CarSim联合平台上进行的仿真表明，与两种不同类型的高级模型预测控制（MPC）控制器相比，新提出的驱动器模型与专家驱动器的匹配性能更好。拟议的驾驶员模型在半自动车辆上具有潜在的应用潜力，可以为自动驾驶提供类似人的特质，这可能是在驾驶员移交转向权限时提高舒适性并改善驾驶中过渡平稳性的重要方面之一。人类共同驾驶的场景。