Human-machine cooperative scheme for car-following control of the connected and automated vehicles

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 H-infinity 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. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

A human-machine cooperative scheme is proposed to address the out-of-the-loop problem of automated driving by assigning different tasks to human drivers and automated driving systems, improving car-following performance, and reducing the operating load of human drivers.