Path tracking is one of the most important aspects of autonomous vehicles. The current research focuses on designing path-tracking controllers taking into account the stability of the yaw and the nonholonomic constraints of the vehicle. In most cases, the lateral controller design relies on identifying a path reference point, the one with the shortest distance to the vehicle giving the current state of the vehicle. That restricts the controller’s ability to handle sudden changes of the trajectory heading angle. The present article proposes a new approach that imitates human behavior while driving. It is based on a discrete prediction model that anticipates the future states of the vehicle, allowing the use of the control algorithm in future predicted states augmented with the current controller output. The performance of the proposed approach is verified through several simulations on V-REP simulator with different types of maneuvers (double lane change, hook road, S road, and curved road) and a wide range of velocities. Predictive Stanley controller was used compared to the original Stanley controller. The obtained results of the proposed control approach show the advantage and the performance of the technique in terms of minimizing the lateral error and ensuring yaw stability by an average of 53% and 22%, respectively.

中文翻译：

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一种使用预测斯坦利横向控制器的路径跟踪算法

路径跟踪是自动驾驶汽车最重要的方面之一。目前的研究重点是设计路径跟踪控制器，同时考虑偏航稳定性和车辆的非完整约束。在大多数情况下，横向控制器设计依赖于确定一个路径参考点，距离车辆最短的那个点给出了车辆的当前状态。这限制了控制器处理轨迹航向角突然变化的能力。本文提出了一种模仿人类驾驶行为的新方法。它基于预测车辆未来状态的离散预测模型，允许在未来预测状态中使用控制算法，并增加当前控制器输出。通过在 V-REP 模拟器上对不同类型的机动（双车道变换、弯道、S 路和弯道）和各种速度进行的多次模拟，验证了所提出方法的性能。与原始 Stanley 控制器相比，使用了 Predictive Stanley 控制器。所提出的控制方法获得的结果显示了该技术在最小化横向误差和确保偏航稳定性平均分别为 53% 和 22% 方面的优势和性能。