Shared control architectures for vehicle steering

Ghasemi, Amir H.; Jayakumar, Paramsothy; Gillespie, R. Brent

doi:10.1007/s10111-019-00560-9

Cited by 21 publications

(9 citation statements)

References 28 publications

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“…Proof. For the control design of the LPV system (18), we consider the parameter-dependent Lyapunov function…”

Section: Lpv Control Design With ∞ -Gain Performancementioning

confidence: 99%

“…whereV (x) is the time-derivative of the Lyapunov function defined in ( 25) along the solution of the closed-loop system (18). Since w ∈ B , it follows from (32…”

Section: Lpv Control Design With ∞ -Gain Performancementioning

confidence: 99%

“…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%

“…These situations arise especially during some extreme maneuvers, such as obstacle avoidance [ 24 ], navigating a sharp curve [ 25 ], and highway lane change [ 26 ], among others. 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 ].…”

Section: Introductionmentioning

confidence: 99%

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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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“…Proof. For the control design of the LPV system (18), we consider the parameter-dependent Lyapunov function…”

Section: Lpv Control Design With ∞ -Gain Performancementioning

confidence: 99%

“…whereV (x) is the time-derivative of the Lyapunov function defined in ( 25) along the solution of the closed-loop system (18). Since w ∈ B , it follows from (32…”

Section: Lpv Control Design With ∞ -Gain Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Nguyen

Rath

et al. 2021

Sensors

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“…Furthermore, in the process of man-computer cooperative driving, the most important component has been the distribution of driving rights and cooperation [4]. Ghasemi et al [5] also agreed that the critical point of man-computer cooperation is to determine the driving rights between the driver and the control system.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Drivers’ Comprehensive Driving Capability Under Man-Computer Cooperative Driving Conditions

et al. 2020

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The effective assessment of drivers' driving capability under the condition of an advanced driver assistance system is of great significance to the precise switching of driving rights between human and machine and the promotion of the development of man-computer collaboration. In this study, real-time collected warning data from bus driver state monitoring system (DSMS) and advanced driver assistance system (ADAS) were utilized to determine the drivers' comprehensive driving capability indicators. The information utility and interaction of the indicators were considered, and an integrated weight method based on standard deviations was proposed. This method was used to combine the entropy weight method and improved analytic network process (ANP), to evaluate the drivers' comprehensive driving capability under man-computer cooperative driving conditions in real time. The results show that the entropy weight method and improved ANP algorithm have good consistency and are significantly correlated and that the integrated weight method is effective and dependable. The top four indicators in the integrated weighting results were eye closure (0.241), yawn (0.210), rapid deceleration (0.186), and lane departure (0.159). Drivers' comprehensive driving capability scores were concentrated in the score range of 1 to 6, with the lowest scores in zones A and B for stages 2, 11 and 21. Therefore, it is necessary to further explore the relationship between driver behavior, vehicle status and road traffic environment within the score range of 1 to 6 so that the man-computer interaction can be optimized and the driver's comprehensive driving capability can be improved.INDEX TERMS advanced driver assistance systems, driving capability, entropy weight method, improved ANP, man-computer cooperative driving.

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