Indirect Shared Control for Cooperative Driving Between Driver and Automation in Steer-by-Wire Vehicles

• Published in: IEEE transactions on intelligent transportation systems Vol. 22; no. 12; pp. 7826 - 7836
• Main Authors: Li, Renjie, Li, Yanan, Li, Shengbo Eben, Zhang, Chaofei, Burdet, Etienne, Cheng, Bo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; automated vehicles; Automation; Autonomous vehicles; Control methods; Cooperative control; Drive by wire; Exact solutions; Haptic interfaces; human-machine interaction; Lane keeping; Man-machine systems; Predictive control; Predictive models; Shared control; steer-by-wire; Steering; Trajectory planning; Wheels
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2020.3010620

It is widely acknowledged that drivers should remain in the control loop before automated vehicles completely meet real-world operational conditions. This paper presents an "indirect shared control" framework for steer-by-wire vehicles, which allows the control authority to be continuously shared between the driver and automation through an weighted-input-summation method. A "best-response" driver steering model based on model predictive control (MPC) for indirect shared control is proposed. Unlike any conventional driver model for manual driving, this model assumes that drivers can learn and incorporate the controller strategy into their internal model for predictive path following. The analytic solution to the driver model is provided to enable off-line simulations. A driving-simulator experiment was conducted to demonstrate the advantages of the indirect shared control system in a highway lane-keeping task. The result showed that the proposed indirect shared control method was effective to improve the subjects' lane-keeping performance and reduce steering control effort. The proposed driver steering model was also validated by the experiment data, which produced a smaller prediction error than the conventional MPC driver model.