The full-fledged development and the practical use of autonomous vehicles (AVs) would be a great technological achievement and would substantially reduce the enormous damages caused by driving accidents to life and property. Technology based companies such as Google and Audi are getting closer to realizing the dream of seeing fully AVs on the road. A vehicle’s severely nonlinear dynamics due to the forces acting between road and vehicle tires, the coupling characteristic, and the uncertainties of parameters such as wheel moment of inertia and vehicle mass have made it rather difficult to approximate a precise mathematical model of vehicle dynamics. In this paper, to overcome these challenges we propose a model-independent control method based on improved adaptive neural controllers for path tracking control of AVs. In the structure of these improved neural controllers, we employ interval type-2 fuzzy sets (IT2FS) as activation functions. Despite the interdependence of a vehicle’s longitudinal and lateral motions, many of the research works on the path tracking of AVs have only focused on lateral motion control. By using the inputs of steering angle and torque, the presented control scheme tackles the simultaneous control of lateral and longitudinal moves. Results obtained from the lateral controller based on an improved neural network (NN) have been analyzed first at a constant velocity of 20 m/s and with/without considering parametric uncertainties. Then the longitudinal controller based on the improved NN is compared with sliding mode and common NN based controllers. Finally, the results obtained by simulating the simultaneous control of lateral and longitudinal motions indicate maximum tracking errors of 0.04 m (for lateral path following) and 0.02 m/s (for longitudinal velocity) and demonstrate the desirable performance of the proposed control approach.

全自动驾驶汽车的全面开发和实际使用将是一项伟大的技术成就，并将大大减少因驾驶事故对生命和财产造成的巨大损失。基于技术的公司（例如Google和Audi）正在接近实现在道路上看到完整的AV的梦想。由于道路和车辆轮胎之间作用的力，耦合特性以及诸如车轮惯性矩和车辆质量之类的参数的不确定性，导致车辆的严重非线性动力学特性，使得近似精确的车辆动力学数学模型相当困难。在本文中，为了克服这些挑战，我们提出了一种基于模型的独立控制方法，该方法基于改进的自适应神经控制器，用于AV的路径跟踪控制。在这些改进的神经控制器的结构中，我们采用间隔2型模糊集（IT2FS）作为激活函数。尽管车辆的纵向和横向运动是相互依存的，但许多有关自动驾驶汽车路径跟踪的研究工作只集中在横向运动控制上。通过使用转向角和扭矩的输入，提出的控制方案解决了横向和纵向运动的同时控制。首先基于20 m / s的恒定速度分析了基于改进神经网络（NN）的横向控制器获得的结果，并且不考虑参数不确定性。然后将基于改进神经网络的纵向控制器与滑模和基于普通神经网络的控制器进行比较。最后，