In this paper, an adaptive neuro-fuzzy predictor-based control (ANFPC) approach with integrated automotive radar and vehicle-to-vehicle (V2V) communication for the design of the cooperative adaptive cruise control (CACC) system is presented, which concerns not only the safety and riding comfort of a vehicle but also enhances its fuel efficiency. This paper consists of two main parts: preceding vehicle state estimation and following vehicle controller. First, the prospective information of the preceding vehicle, such as position, velocity, and acceleration, can be derived through radar sensor, and the control force of the preceding vehicle can be transmitted to the following vehicles through V2V communication. A Takagi–Sugeno fuzzy model is then utilized to estimate the preceding vehicle model, and the predicted state sequence of the preceding vehicle can be obtained. Second, based on these predicted data, the following vehicle is controlled by the proposed ANFPC scheme to maintain each vehicle within the desired distance headway and thus achieve string stability of vehicle platooning and fuel efficiency. The experimental results on the CarSim environment show that the proposed control strategy for the CACC system can significantly reduce the fuel consumption while ensuring driving comfort and safety.

中文翻译：

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基于自适应神经模糊预测器的协同自适应巡航控制系统控制

在本文中，提出了一种具有集成汽车雷达和车对车 (V2V) 通信的基于自适应神经模糊预测器的控制 (ANFPC) 方法，用于设计协作自适应巡航控制 (CACC) 系统，该方法不涉及不仅提高了车辆的安全性和乘坐舒适性，而且还提高了其燃油效率。本文主要由两部分组成：前车状态估计和后车控制器。首先，可以通过雷达传感器推导出前车的位置、速度、加速度等前瞻信息，通过V2V通信将前车的控制力传递给后车。然后使用 Takagi-Sugeno 模糊模型来估计前车模型，并且可以得到前车的预测状态序列。其次，基于这些预测数据，后面的车辆由所提出的 ANFPC 方案控制，以将每辆车保持在所需的车头距离内，从而实现车辆排队和燃油效率的串稳定性。在 CarSim 环境下的实验结果表明，所提出的 CACC 系统控制策略可以在保证驾驶舒适性和安全性的同时显着降低油耗。