Design and Testing of a Controller for Autonomous Vehicle Path Tracking Using GPS/INS Sensors

Kang, Juyong; Hindiyeh, Rami Y.; Moon, Seungwuk; Gerdes, J. Christian; Yi, Kyongsu

doi:10.3182/20080706-5-kr-1001.00355

Cited by 28 publications

(37 citation statements)

References 4 publications

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“…Note that, in contrast with [17], one extra state δ appears in (21). The extra state comes from the steering dynamics (18) so that the control exploits the information about the actuation authority.…”

Section: Linearized Modelmentioning

confidence: 99%

“…Following a similar approach as in [17], an optimal finite preview control method is used to develop a steering controller. The intention is to eliminate lateral and yaw angle error through a combination of feedback and feed-forward control.…”

Section: Steering Controllermentioning

confidence: 99%

“…By using small angle approximation for steering angle (less than 5 degrees for highway driving situations), the tire angle is [17] …”

Section: Linearized Modelmentioning

confidence: 99%

“…The lateral tire forces are assumed to be linear functions of slip angles α i and cornering stiffness Cy i [17] F y,i = 2C y,i α i .…”

Section: Vehicle Dynamicsmentioning

confidence: 99%

“…Yaw angle error is denoted with ε and defined as the difference between the yaw angle of the vehicle ψ and the desired yaw angle as dictated by the desired path ψ d . The rate of changes of the lateral position error and the yaw angle error are defined as [17] yr = υy + υxε,…”

Section: Linearized Modelmentioning

confidence: 99%

See 4 more Smart Citations

Integration of auto‐steering with adaptive cruise control for improved cornering behaviour

Idriz

Rachman

Baldi

2017

IET Intelligent Transport Systems

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Section: Linearized Modelmentioning

confidence: 99%

Section: Steering Controllermentioning

confidence: 99%

“…By using small angle approximation for steering angle (less than 5 degrees for highway driving situations), the tire angle is [17] …”

Section: Linearized Modelmentioning

confidence: 99%

“…The lateral tire forces are assumed to be linear functions of slip angles α i and cornering stiffness Cy i [17] F y,i = 2C y,i α i .…”

Section: Vehicle Dynamicsmentioning

confidence: 99%

Section: Linearized Modelmentioning

confidence: 99%

See 3 more Smart Citations

Integration of auto‐steering with adaptive cruise control for improved cornering behaviour

Idriz

Rachman

Baldi

2017

IET Intelligent Transport Systems

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Robust Approximate Constraint‐Following Control for Autonomous Vehicle Platoon Systems

Zhao¹,

Chen²,

Zhao³

2017

Asian Journal of Control

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We consider an autonomous vehicle platoon system consisting of N + 1 vehicles in the presence of modeling uncertainty. The uncertainty may be due to parameter variations, aerodynamics, external disturbances, etc., which is nonlinear and time-varying. Subject to the collision avoidance consideration, the original state is one-sided restricted. To resolve this restriction, we propose a state transformation to convert the bounded state into a globally unbounded state. Furthermore, motivated by the properties of artificial swarm systems, we incorporate the swarm system performance into the platoon system by treating it as a d'Alembert's constraint. By the Udwadia and Kalaba's approach, we obtain the analytic (closed-form) expression of the constraint force. Based on this, a class of robust controls for each vehicle (except the leading vehicle) is proposed to drive the platoon system to follow the ideal swarm model. Four major system performances are accomplished: (i) compact vehicle formation, (ii) collision avoidance, (iii) stable platoon system formation, (iv) global behavior.

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Statistical modeling of the sensory‐perception process of computational driver models for vehicle simulations

Rah

Park

2010

Hum Ftrs & Erg Mfg Svc

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This article describes a new approach in developing computational driver models in vehicle simulations for evaluating intelligent driving support systems (IDSSs). As IDSSs become more intelligent and provide active safety controls, a more comprehensive understanding of a driver's cognitive processes is required as well as vehicle dynamics. One hundred thirty drivers participated in a psychophysical experiment of velocity estimation at 71 checkpoints on a road section. The perceived velocity, as a dependent variable, was modeled using analysis of covariance (ANCOVA) with respect to the driver's fundamental human factors (i.e., age, gender, and driving experience) as independent variables. As a result, eight different perceived velocity models were defined as part of the cognitive processes with quantitative covariates in different modalities (i.e., radius of road, level of interior noise, as well as vehicle velocity). Moreover, driver models implemented with different perceptual processes also exhibited considerable changes in the results of vehicle simulations on double lane change maneuvers. The results presented in this study suggest that defining the perceptual processes of different groups of drivers is an essential process in developing vehicle simulation environments for evaluating IDSS. Consequently, the results from this study may provide meaningful data for early stages of new vehicle design. C 2010 Wiley Periodicals, Inc.

show abstract

Design and Testing of a Controller for Autonomous Vehicle Path Tracking Using GPS/INS Sensors

Cited by 28 publications

References 4 publications

Integration of auto‐steering with adaptive cruise control for improved cornering behaviour

Integration of auto‐steering with adaptive cruise control for improved cornering behaviour

Robust Approximate Constraint‐Following Control for Autonomous Vehicle Platoon Systems

Statistical modeling of the sensory‐perception process of computational driver models for vehicle simulations

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