To address the out-of-the-loop problem of the automated driving, a human–machine cooperative scheme for car-following control of the connected and automated vehicles (CAVs) is proposed. The proposed scheme can keep the drivers always in the loop and improve the car-following performance. To be specific, the driving automation system (artificial driver) is assigned to the task of velocity tracking and the human driver is responsible for headway adjustment. For the velocity tracking task, a feedforward–feedback control strategy was designed firstly by considering the advantages of the accurate perception and communication of CAVs, then an ${\mathcal{H}}_{\infty }$ suboptimal control method was developed to optimize the controller parameters according to the desired performance index, further the controller was fine-tuned based on the idea of human-simulated intelligent control (HSIC) to improve the dynamic performance of the velocity tracking. For the operator’s headway adjusting task, the stability analysis based on the Lyapunov function proved that the simple proportional feedback control can be assumed by the driver to ensure the system stability under the cooperation of automated velocity tracking. The experiments based on the driving simulator demonstrated that human–machine cooperative scheme for car-following can reduce the tracking error of vehicle distance effectively, and the human driver can be kept in the control loop with a smaller operating load.

为了解决自动驾驶的“圈外”问题，提出了一种人机协作方案，用于对联网的自动驾驶汽车（CAV）进行跟车控制。提出的方案可以使驾驶员始终处于循环状态，并提高了跟车性能。具体而言，将驾驶自动化系统（人工驾驶员）分配给速度跟踪任务，而人工驾驶员负责车头速度调整。对于速度跟踪任务，首先通过考虑对CAV的准确感知和交流的优势来设计前馈-反馈控制策略，然后${\mathcal{H}}_{\infty }$开发了次优控制方法，以根据所需的性能指标优化控制器参数，并根据人工模拟智能控制（HSIC）的思想对控制器进行了微调，以改善速度跟踪的动态性能。对于驾驶员的行程调节任务，基于李雅普诺夫函数的稳定性分析证明，驾驶员可以采用简单的比例反馈控制，以确保在自动速度跟踪的配合下系统的稳定性。基于驾驶模拟器的实验表明，人机协作的汽车跟随方案可以有效地减少行车距离的跟踪误差，并且可以将驾驶员保持在控制回路中，并且操作负荷较小。