Driver Model Validation through Interaction with Varying Levels of Haptic Guidance

Zhao, Yishen; Pano, Béatrice; Chevrel, Philippe; Claveau, Fabien; Mars, Franck

doi:10.1109/smc42975.2020.9283382

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…An experimental study conducted by Griffiths et al [39] on 11 drivers showed that haptic guidance applied to the steering wheel enhanced the path direction by 30% and improved the reaction time for a specific task by 18 ms and the visual demand by 29%. Zhao et al [40] represented a haptic guidance system where the steering duty is shared between the driver and the haptic system. Γ d , Γ a , α, and ρ represented, respectively, the driver steering torque, the haptic guidance torque, the sharing level, and the road curvature as entirely describe in ( [40], Figure 1) .…”

Section: Haptic Guidance In-vehicle Controlmentioning

confidence: 99%

“…Zhao et al [40] represented a haptic guidance system where the steering duty is shared between the driver and the haptic system. Γ d , Γ a , α, and ρ represented, respectively, the driver steering torque, the haptic guidance torque, the sharing level, and the road curvature as entirely describe in ( [40], Figure 1) .…”

Section: Haptic Guidance In-vehicle Controlmentioning

confidence: 99%

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Review on Haptic Assistive Driving Systems Based on Drivers’ Steering-Wheel Operating Behaviour

Tientcheu

Djouani

2022

Electronics

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With the availability of modern assistive techniques, ambient intelligence, and the Internet of Things (IoT), various innovative assistive environments have been developed, such as driving assistance systems (DAS), where the human driver can be provided with physical and emotional assistance. In this human–machine collaboration system, haptic interaction interfaces are commonly employed because they provide drivers with a more manageable way to interact with other components. From the view of control system theory, this is a typical closed-loop feedback control system with a human in the loop. To make such a system work effectively, both the driving behaviour factors, and the electrical–mechanical components should be considered. However, the main challenge is how to deal with the high degree of uncertainties in human behaviour. This paper aims to provide an insightful overview of the relevant work. The impact of various types of haptic assistive driving systems (haptic guidance and warning systems) on driving behaviour performance is discussed and evaluated. In addition, various driving behaviour modelling systems are extensively investigated. Furthermore, the state-of-the-art driving behaviour controllers are analysed and discussed in driver–vehicle–road systems, with potential improvements and drawbacks addressed. Finally, a prospective approach is recommended to design a robust model-free controller that accounts for uncertainties and individual differences in driving styles in a haptic assistive driving system. The outcome indicated that the haptic feedback system applied to the drivers enhanced their driving performance, lowered their response time, and reduced their mental workload compared to a system with auditory or visual signals or without any haptic system, despite some annoyances and system conflicts. The driving behaviour modelling techniques and the driving behaviour control with a haptic feedback system have shown good matching and improved the steering wheel’s base operation performance. However, mathematical principles, a statistical approach, and the lack of consideration of the individual differences in the driver–vehicle–road system make the modelling and the controller less robust and inefficient for different driving styles.

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Section: Haptic Guidance In-vehicle Controlmentioning

confidence: 99%

Section: Haptic Guidance In-vehicle Controlmentioning

confidence: 99%

Review on Haptic Assistive Driving Systems Based on Drivers’ Steering-Wheel Operating Behaviour

Tientcheu

Djouani

2022

Electronics

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“…This strategy is interesting as it used the sharing level between the human driver and the assistance explicitly. Moreover, the shared control proposed in (Zhao et al, 2020) is much more flexible since this work follow-up the study in (Pano et al, 2019). It aims to integrate the driver's adaptation in the level of sharing over the driving task using cybernetic driver model.…”

Section: State Of the Artmentioning

confidence: 99%

Online driver model parameter identification using the Lyapunov approach based shared control

Oudainia¹,

Sentouh²,

Nguyen³

et al. 2022

Front. Control Eng.

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The work described in this paper proposes a new conflict minimisation strategy in shared driving control for lane keeping systems (LKS) in intelligent vehicles. This strategy takes into account a dynamic driver model, where the driver’s parameters are identified online using the Lyapunov approach. The design of an adaptive shared controller is based on the dynamic parameters of the driver model which changes according to the driver and the situation encountered. Based on Lyapunov stability arguments, the overall asymptotic stability of the closed-loop control system with the adaptive driver model and the variation of the vehicle speed is proved and an LMI optimization is used to formulate the control design. The simulation results, conducted with the SHERPA dynamic car simulator under real-world driving situations, show the importance of integrating a dynamic driver model in the controller design in order to decrease the conflict between the driver and the lane keeping system and to ensure the safety of the vehicle as well as to increase the confidence and acceptability of the driver.

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“…In addition, these models can be used for the realization of controllers in haptic guidance systems [19]. Controllers based on driver models have many advantages, such as reduction of undesired steering conflicts with the guidance system, smooth authority transition, and consideration of the driver's state [20][21][22][23][24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Driving with a Haptic Guidance System in Degraded Visibility Conditions: Behavioral Analysis and Identification of a Two-Point Steering Control Model

et al. 2022

Self Cite

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The objective of this study is to determine the ability of a two-point steering control model to account for the influence of a haptic guidance system in different visibility conditions. For this purpose, the lateral control of the vehicle was characterized in terms of driving performance as well as through the identification of anticipation and compensation parameters of the driver model. The hypothesis is that if the structure of the model is valid in the considered conditions, the value of the parameters will change in coherence with the observed behavior. The results of an experiment conducted on a driving simulator demonstrate that the identified model can account for the cumulative influence of the haptic guidance system and degraded visibility. The anticipatory gain is sensitive to changes in driving conditions that have a direct influence on the produced trajectory, and the compensatory gain is sensitive to a decrease in the variability of the lateral position. However, a model with only the steering wheel angle as output is not able to determine whether the change in lateral position variability is due to the driver’s lack of anticipation or to the assistance provided by the haptic guidance system.

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Driver Model Validation through Interaction with Varying Levels of Haptic Guidance

Cited by 5 publications

References 11 publications

Review on Haptic Assistive Driving Systems Based on Drivers’ Steering-Wheel Operating Behaviour

Review on Haptic Assistive Driving Systems Based on Drivers’ Steering-Wheel Operating Behaviour

Online driver model parameter identification using the Lyapunov approach based shared control

Driving with a Haptic Guidance System in Degraded Visibility Conditions: Behavioral Analysis and Identification of a Two-Point Steering Control Model

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