Compared to traditional adaptive cruise control (ACC), cooperative ACC (CACC) can improve the response sensitivity of the following vehicles by using additional information, e.g., the acceleration of preceding vehicles, that is transmitted via inter-vehicle wireless communications. Thus, a platoon with CACC mode obtains a shorter time headway, thereby increasing road throughput while guaranteeing traffic safety. However, delays are common in wireless communication due to complex traffic conditions. The degradation of wireless communication significantly influences the string stability that refers to the attenuation of disturbance in the upstream direction of a platoon. Therefore, this study proposes the use of a deep learning method, i.e., the long short-term memory (LSTM) neural network, to predict the acceleration of the preceding vehicle by using data from onboard radar sensors. It provides the CACC platoon with another option to obtain additional information if the quality of wireless communication worsens. This type of CACC is referred to the LSTM control. Simulations proved the applicability of the LSTM control by using the next generation simulation program (NGSIM) data, in which the accuracy (goodness-of-fit index) of the LSTM prediction reached 0.766, and the LSTM control can compensate the communication delay to maintain string stability when the communication delay exceeded 0.115 s.

与传统的自适应巡航控制（ACC）相比，协作式ACC（CACC）可以通过使用其他信息（例如通过车辆间无线通信传输的先前车辆的加速度）来提高后续车辆的响应灵敏度。因此，具有CACC模式的排可获得较短的行驶时间，从而在确保交通安全的同时增加了道路通行量。然而，由于复杂的交通状况，延迟在无线通信中很常见。无线通信的降级会显着影响弦的稳定性，弦稳定性是指在排上游方向上干扰的减弱。因此，本研究建议使用深度学习方法，即长短期记忆（LSTM）神经网络，通过使用车载雷达传感器的数据来预测前车的加速度。如果无线通信质量变差，它为CACC排提供了另一种选择来获取其他信息。此类CACC被称为LSTM控制。仿真通过使用下一代仿真程序（NGSIM）数据证明了LSTM控制的适用性，其中LSTM预测的准确性（拟合优度指数）达到0.766，并且LSTM控制可以补偿通信延迟以保持当通讯延迟超过0.115 s时，灯串保持稳定。