Decision-making in driver-automation shared control: A review and perspectives

Wang, Wenshuo; Na, Xiaoxiang; Cao, Dongpu; Gong, Jianya; Jiang, Xi; Xing, Yang; Wang, Fei‐Yue

doi:10.1109/jas.2020.1003294

Cited by 120 publications

(46 citation statements)

References 227 publications

(482 reference statements)

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“…Shared control can be divided into direct shared control and indirect shared control [104]. Indirect shared control is also called coordinated control in some areas of literature [5].…”

Section: A Decision-making In Different Control Modesmentioning

confidence: 99%

A Review of Human–Machine Cooperation in the Robotics Domain

Yang

Zhu

Chen

2022

IEEE Trans. Human-Mach. Syst.

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Artificial intelligence (AI) technology has greatly expanded human capabilities through perception, understanding, action, and learning. The future of AI depends on cooperation between humans and AI. In addition to a fully automated or manually controlled machine, a machine can work in tandem with a human with different levels of assistance and automation. Machines and humans cooperate in different ways. Three strategies for cooperation are described in this article, as well as the nesting relationships among different control methods and cooperation strategies. Based on human thinking and behavior, a hierarchical human-machine cooperation (HMC) framework is improved and extended to design safe, efficient, and attractive systems. We review the common methods of perception, decision-making, and execution in the HMC framework. Future applications and trends of HMC are also discussed.

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“…Shared control can be divided into direct shared control and indirect shared control [104]. Indirect shared control is also called coordinated control in some areas of literature [5].…”

Section: A Decision-making In Different Control Modesmentioning

confidence: 99%

A Review of Human–Machine Cooperation in the Robotics Domain

Yang

Zhu

Chen

2022

IEEE Trans. Human-Mach. Syst.

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“…However, under unpredictable behaviors and characteristics of the human driver in an open driving environment, the design of effective controllers for DASs of semi-autonomous vehicles is known as a challenging problem [ 11 , 12 , 13 ]. To deal with this challenge, various control schemes have been proposed under the purview of shared control [ 14 , 15 , 16 ], i.e., the human driver and the automation cooperates to control the vehicle [ 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…Shared control architectures, considering the HMI management directly in the control design process, have emerged as a promising solution to deal with the driver-automation conflict issue appeared in the driving control process of semi-autonomous vehicles [13,18]. The allocation of the control authority between the automation and the human driver has been proposed in several works, see for instance [14,15,24,[27][28][29]. Further research has highlighted that integrating the human characteristics, such as driving skill, style, and workload, in the control loop significantly improves the HMI management and the driving performance [13,23,30].…”

Section: Introductionmentioning

confidence: 99%

Human-Machine Shared Driving Control for Semi-Autonomous Vehicles Using Level of Cooperativeness

Nguyen

Rath

et al. 2021

Sensors

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This paper proposes a new haptic shared control concept between the human driver and the automation for lane keeping in semi-autonomous vehicles. Based on the principle of human-machine interaction during lane keeping, the level of cooperativeness for completion of driving task is introduced. Using the proposed human-machine cooperative status along with the driver workload, the required level of haptic authority is determined according to the driver’s performance characteristics. Then, a time-varying assistance factor is developed to modulate the assistance torque, which is designed from an integrated driver-in-the-loop vehicle model taking into account the yaw-slip dynamics, the steering dynamics, and the human driver dynamics. To deal with the time-varying nature of both the assistance factor and the vehicle speed involved in the driver-in-the-loop vehicle model, a new ℓ∞ linear parameter varying control technique is proposed. The predefined specifications of the driver-vehicle system are guaranteed using Lyapunov stability theory. The proposed haptic shared control method is validated under various driving tests conducted with high-fidelity simulations. Extensive performance evaluations are performed to highlight the effectiveness of the new method in terms of driver-automation conflict management.

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“…Wang et al [20] considered diverse driving styles and devised a shared steering control law using an exponential function to lighten the burden on the driver when turning. Wang et al [21] introduced the architecture of humanmachine shared control, driver modeling and interaction strategy under driver-vehicle shared schemes, and further discussed the challenges and opportunities in the future. Nevertheless, human-machine shared control is mostly used in relatively stable scenarios such as lane keeping and does not consider the roll motion of vehicle [22], and there is a lack of research on shared control under emergency scenarios.…”

Section: Introductionmentioning

confidence: 99%

Roll Stability and Path Tracking Control Strategy Considering Driver in the Loop

Xuan-yao

2021

IEEE Access

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A study pointed out that the delay time of the driver's nervous system has a significant effect on the roll stability of the vehicle. However, the existing researches on vehicle rollover prevention control rarely consider the influence of driver factors on vehicle roll stability. Aiming at this problem, a vehicle roll stability and path tracking control strategy considering driver in the loop is proposed to assist different types of drivers. It includes the supervisory decision layer and execution layer. The supervisory decision layer selects the corresponding control mode according to the driver's steering wheel angle change rate, path tracking deviation and vehicle roll stability information. The execution layer includes three modes: human-machine shared steering, active braking and integrated chassis control. The human-machine shared steering and active braking modes assist the driver to improve the roll stability and path tracking accuracy. The integrated chassis control mode is used for the automatic driving of vehicles under emergency conditions. Simulation results show that the proposed control strategy can effectively improve the vehicle roll stability and path tracking accuracy, and reduce the driver's operating burden. INDEX TERMS Roll stability, path tracking, human-machine sharing, coordinated control, integrated control.

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Decision-making in driver-automation shared control: A review and perspectives

Cited by 120 publications

References 227 publications

A Review of Human–Machine Cooperation in the Robotics Domain

A Review of Human–Machine Cooperation in the Robotics Domain

Human-Machine Shared Driving Control for Semi-Autonomous Vehicles Using Level of Cooperativeness

Roll Stability and Path Tracking Control Strategy Considering Driver in the Loop

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