Recursive Identification of Cornering Stiffness Parameters for an Enhanced Single Track Model

Lundquist, Christian; Schön, Thomas B.

doi:10.3182/20090706-3-fr-2004.00287

Cited by 29 publications

(14 citation statements)

References 9 publications

(12 reference statements)

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“…The cornering stiffness, i.e., C, describes the amount of lateral force generated for a given tire-slip angle and thereby directly influence the lateral dynamics of the motion model. Both road surface and tire characteristics influence C (see, e.g., [38] and [39] for online estimation methods). The mounting angles of the camera on the vehicle decide how the motion model aligns with the image feature observations.…”

Section: Sensitivity To Model Parametersmentioning

confidence: 99%

Reliable Vehicle Pose Estimation Using Vision and a Single-Track Model

Nilsson

Fredriksson

Odblom

2014

IEEE Trans. Intell. Transport. Syst.

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This paper examines the problem of estimating vehicle position and direction, i.e., pose, from a single vehicle-mounted camera. A drawback of pose estimation using vision only is that it fails when image information is poor. Consequently, other information sources, e.g., motion models and sensors, may be used to complement vision to improve the estimates. We propose to combine standard in-vehicle sensor data and vehicle motion models with the accuracy of local visual bundle adjustment. This means that pose estimates are optimized with regard not only to observed image features but also to a single-track vehicle model and standard in-vehicle sensors. The described method has been experimentally tested on challenging data sets at both low and high vehicle speeds as well as a data set with moving objects. The vehicle motion model in combination with in-vehicle sensors exhibit good accuracy in estimating planar vehicle motion. Results show that this property is preserved, when combining these information sources with vision. Furthermore, the accuracy obtained from vision-only in direction estimation is improved, primarily in situations in which there are few matched visual features.

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Section: Sensitivity To Model Parametersmentioning

confidence: 99%

Reliable Vehicle Pose Estimation Using Vision and a Single-Track Model

Nilsson

Fredriksson

Odblom

2014

IEEE Trans. Intell. Transport. Syst.

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“…Recursive least square methods can be successfully applied when the unknown parameters are the linear coefficients of the system dynamic equations. In such cases, quite usual in literature [43][44][45][46], the model can be rearranged as a linear time-varying system with respect to the parameters to be estimated, whereas the other terms, supposed measurable, are functions either of the state or of the output variables.…”

Section: Mechanical Characteristics Estimationmentioning

confidence: 99%

Recursive Least Squares Filtering Algorithms for On-Line Viscoelastic Characterization of Biosamples

et al. 2018

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The mechanical characterization of biological samples is a fundamental issue in biology and related fields, such as tissue and cell mechanics, regenerative medicine and diagnosis of diseases. In this paper, a novel approach for the identification of the stiffness and damping coefficients of biosamples is introduced. According to the proposed method, a MEMS-based microgripper in operational condition is used as a measurement tool. The mechanical model describing the dynamics of the gripper-sample system considers the pseudo-rigid body model for the microgripper, and the Kelvin–Voigt constitutive law of viscoelasticity for the sample. Then, two algorithms based on recursive least square (RLS) methods are implemented for the estimation of the mechanical coefficients, that are the forgetting factor based RLS and the normalised gradient based RLS algorithms. Numerical simulations are performed to verify the effectiveness of the proposed approach. Results confirm the feasibility of the method that enables the ability to perform simultaneously two tasks: sample manipulation and parameters identification.

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“…The load transfer to the front axle when braking or to the outer wheels when driving trough a curve influences the parameter value. A model considering these influences is given in [44]. Following this brief introduction to the ego vehicle geometry, we are now ready to give an expression describing the evolution of yaw angle ψ E and the float angle β over timë…”

Section: Ego Vehiclementioning

confidence: 99%

“…Since the cornering stiffness is a parameter which describes the properties between road and tire it has to be estimated for the given set of measurements. An on-line method to estimate the cornering stiffness parameter using recursive least square is presented in [44]. However, in the present work an exhaustive search was accomplished off-line using a batch of measurements to estimate C α f and C αr .…”

Section: Parameter Estimation and Filter Tuningmentioning

confidence: 99%

Joint ego-motion and road geometry estimation

Lundquist

Schön

2011

Information Fusion

Self Cite

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We provide a sensor fusion framework for solving the problem of joint ego-motion and road geometry estimation. More specifically we employ a sensor fusion framework to make systematic use of the measurements from a forward looking radar and camera, steering wheel angle sensor, wheel speed sensors and inertial sensors to compute good estimates of the road geometry and the motion of the ego vehicle on this road. In order to solve this problem we derive dynamical models for the ego vehicle, the road and the leading vehicles. The main difference to existing approaches is that we make use of a new dynamic model for the road. An extended Kalman filter is used to fuse data and to filter measurements from the camera in order to improve the road geometry estimate. The proposed solution has been tested and compared to existing algorithms for this problem, using measurements from authentic traffic environments on public roads in Sweden. The results clearly indicate that the proposed method provides better estimates.

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Recursive Identification of Cornering Stiffness Parameters for an Enhanced Single Track Model

Cited by 29 publications

References 9 publications

Reliable Vehicle Pose Estimation Using Vision and a Single-Track Model

Reliable Vehicle Pose Estimation Using Vision and a Single-Track Model

Recursive Least Squares Filtering Algorithms for On-Line Viscoelastic Characterization of Biosamples

Joint ego-motion and road geometry estimation

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