Considering Variable Road Geometry in Adaptive Vehicle Speed Control

Yan, Xinping; Zhang, Rui; Ma, Jie; Ma, Yulin

doi:10.1155/2013/617879

Cited by 11 publications

(7 citation statements)

References 24 publications

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“…In order to address the combined longitudinal and lateral control, the state-space representation of the IV dynamic system is adopted to build the multiple-input multiple-output (MIMO) control models [20][21][22][23][24] . However, the implementation of these MIMO controllers (e.g., prediction control [20] or H ∞ control [25] ) often imposes offline computation and strict constraints in both simulations and experiments.…”

Section: Energy Dissipation Based Longitudinal and Lateral Couplingmentioning

confidence: 99%

“…In order to simplify the model derivation and the computational burden, it is assumed that roll, pitch, and bounce motions are negligible, the effect of suspension on the tire axels is also negligible, and brake, throttle, and steering dynamics are discounted. Thus, in this specific research objective, the displacement is used to establish the combined longitudinal and lateral IV model with 3 DOFs, which can be expressed in the following second order derivative form [23,24]…”

Section: B Longitudinal and Lateral Coupling Vehicle Modelmentioning

confidence: 99%

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Energy Dissipation Based Longitudinal and Lateral Coupling Control for Intelligent Vehicles

Zhang

et al. 2018

IEEE Intell. Transport. Syst. Mag.

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This paper proposes a combined longitudinal and lateral control approach for an intelligent vehicle system based on energy dissipation. The vehicle system dynamics resembles a series of mass/spring/damper systems that are dissipative, i.e., the energy of the system decays to zero eventually. Thus, the nonlinear-optimal longitudinal and lateral coupling control problem of the intelligent vehicle system is transformed into a dissipative control design based on an energy storage function. To satisfy the γ-performance, with respect to the quadratic supply rate, the storage function is developed by using a back-stepping based Lyapunov method and a step-by-step improvement of performance bounds. A dissipative feedback control law is formulated by successive approximation based on the step-by-step reduction of the value of γ. The results of the adaptive vehicle control simulations and test-bed experiments are provided and verified by the respective comparison of energy consumption on different values of γ and speed adaption under different road geometries.

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Section: Energy Dissipation Based Longitudinal and Lateral Couplingmentioning

confidence: 99%

Section: B Longitudinal and Lateral Coupling Vehicle Modelmentioning

confidence: 99%

Energy Dissipation Based Longitudinal and Lateral Coupling Control for Intelligent Vehicles

Zhang

et al. 2018

IEEE Intell. Transport. Syst. Mag.

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“…One way of representing the road geometric data is to define data points along the road centerline with specified intervals corresponding to the desired road profile accuracy level. There are several interpolation methods to construct new data points (see, e.g, [10], [11], [12]). However, the interval determination of data points can lead to either overestimated or underestimated accuracy levels for different road segments.…”

Section: Road Geometry and Traffic Modelsmentioning

confidence: 99%

Nonlinear model predictive extended eco-cruise control for battery electric vehicles

Sajadi-Alamdari

Voos

Darouach

2016

2016 24th Mediterranean Conference on Control and Automation (MED)

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Abstract-Battery Electric Vehicles are becoming a promising technology for road transportation. However, the main disadvantage is the limited cruising range they can travel on a single battery charge. This paper presents a novel extended ecological cruise control system to increase the autonomy of an electric vehicle by using energy-efficient driving techniques. Driven velocity, acceleration profile, geometric and traffic characteristics of roads largely affect the energy consumption. An energy-efficient velocity profile should be derived based on anticipated optimal actions for future events by considering the electric vehicle dynamics, its energy consumption relations, traffic and road geometric information. A nonlinear model predictive control method with a fast numerical algorithm is adapted to determine proper velocity profile. In addition, a novel model to describe the energy consumption of a seriesproduction electric vehicle is introduced. The hyperfunctions concept is used to model traffic and road geometry data in a new way. The proposed system is simulated on a test track scenario and obtained results reveal that the extended ecological cruise control can significantly reduce the energy consumption of an electric vehicle.

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Section: Introductionmentioning

confidence: 99%

“…When a vehicle moves along a curved lane, accurate estimation of road curvature is extremely important for steering control of the lane-keeping system or longitudinal motion control of the speed adaptive control system. [1][2][3] Model-based methods are used extensively in lane recognition. The main idea in a model-based method is to convert lane detection to the solution of specific geometric models.…”

Section: Introductionmentioning

confidence: 99%

Curve recognition algorithm based on edge point curvature voting

Wang

Wei

Wang

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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In this paper, a new curve-lane recognition algorithm is proposed. The algorithm uses edge point curvature voting to determine the region of interest based on near-vision straight-lane information. First, information is detected in the near-vision area regarding the straight lines to the left and right of the current lane. Near-vision lane-line extraction includes lane image filtering, as well as edge detection of the region of interest below the vanishing line. The vanishing point is positioned by determining the position of the edge point and distribution of the direction angle. In addition, the straight line is extracted based on the position of the vanishing point. The straight lines that are constructed for the current lane in this way are selected and used as supplementation, in combination with the lane model. Next, the road curvature range isometry is divided into multiple subdivision regions. The near-vision lane straight-line curvature parameters extending from each edge point in the region of interest are computed by combining the straight-line near-vision lane information with the curve lane model in the pixel coordinate system. Subsequently, voting and counting are carried out for the curvature regions of each edge point to which the corresponding curvature computing values belong. Finally, the counting maximum from the corresponding curvature regions of the straight lines located to the left and right of the current lane are searched for, and the curvature region is converted, to obtain the lane line corresponding to the curvature parameter values. Experimental results indicate that the proposed curve-lane recognition algorithm can effectively detect the curve lanes of different curvatures. The results also indicate that the proposed curve detection method is highly accurate, and the algorithm is very robust in different environments.

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Considering Variable Road Geometry in Adaptive Vehicle Speed Control

Cited by 11 publications

References 24 publications

Energy Dissipation Based Longitudinal and Lateral Coupling Control for Intelligent Vehicles

Energy Dissipation Based Longitudinal and Lateral Coupling Control for Intelligent Vehicles

Nonlinear model predictive extended eco-cruise control for battery electric vehicles

Curve recognition algorithm based on edge point curvature voting

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