Abstract: To address the issues of freezing of preceding-vehicle state information for cooperative adaptive cruise control (CACC) vehicles after communication interruption and inaccuracy of preceding-vehicle information in model predictive control (MPC), a CACC-MPC control method based on temporal convolutional network (TCN) and velocity residual prediction of the preceding-vehicle was proposed. First, a longitudinal discrete prediction model of CACC platoon, consisting of one leading vehicle and three following vehicles, was established. And communication link state, packet reception state and information age were introduced to describe the degradation process of preceding-vehicle information after communication interruption. Then, a gated baseline speed was constructed according to communication credibility, and a historical feature sequence was formed by use of the status of the ego vehicle, radar observations, communication maintenance status and communication degradation information. TCN was used to predict the speed residual and combined with the gated baseline speed to generate the preceding-vehicle speed preview sequence required by MPC. Finally, based on Simulink/CarSim co-simulation platform, verification was conducted under conditions such as normal communication, communication delay, superposition of communication delay and packet loss, as well as communication interruption of different durations. The results show that after communication interruption, the communication retain speed cannot reflect the subsequent movement changes of the navigation vehicle, and the proposed method can reduce the influence of frozen communication information on preceding-vehicle speed preview, maintain a basic safe spacing from the followed vehicles, and reduce the control input and its fluctuation of change rate, improving the speed following, vehicle distance maintenance and control smoothness of the vehicle queue under communication degradation conditions.

Key words: vehicle engineering; cooperative adaptive cruise control; communication interruption; temporal convolutional network; preceding-vehicle speed preview; model predictive control

摘要： 针对协同式自适应巡航控制（CACC）车辆在通信完全中断后前车状态信息冻结、模型预测控制（MPC）前车信息失准的问题，提出一种基于时间卷积网络（TCN）前车速度残差预测的CACC-MPC控制方法。首先，建立由1辆领航车和3辆跟随车组成的CACC车队纵向离散预测模型，并引入通信链路状态、数据包接收状态和信息年龄描述通信中断后前车信息的退化过程；其次，基于通信可信度构造门控基线速度，利用本车状态、雷达观测、通信保持状态和通信退化信息形成历史特征序列，通过TCN预测前车速度残差，并与门控基线速度融合生成MPC所需的前车速度预瞄序列；最后，基于Simulink/CarSim联合仿真平台，在正常通信、通信延迟、通信延迟与丢包叠加以及不同时长通信中断等工况下进行验证。结果表明：通信中断后通信保持速度无法反映领航车后续运动变化，所提方法能够降低冻结通信信息对前车速度预瞄的影响，使跟随车辆保持基本安全车距，并减小控制输入及其变化率波动，改善通信退化条件下车辆队列的速度跟随、车距保持和控制平顺性。

关键词: 车辆工程；协同式自适应巡航控制；通信中断；时间卷积网络；前车速度预瞄；模型预测控制