Automated Vehicle Attitude and Lateral Velocity Estimation Using a 6-D IMU Aided by Vehicle Dynamics

In areas of large lateral variations in velocity, stacking velocities computed on the basis of hyperbolic moveout can differ substantially from the actual root mean square (rms) velocities. This paper addresses the problem of obtaining rms or migration velocities from stacking velocities in such areas. The first‐order difference between the stacking and the vertical rms velocities due to lateral variations in velocity are shown to be related to the second lateral derivative of the rms slowness [Formula: see text]. Approximations leading to this relation are straight raypaths and that the vertical rms slowness to a given interface can be expressed as a second‐order Taylor series expansion in the midpoint direction. Under these approximations, the effect of the first lateral derivative of the slowness on the traveltime is negligible. The linearization of the equation relating the stacking and true velocities results in a set of equations whose inversion is unstable. Stability is achieved, however, by adding a nonphysical fourth derivative term which affects only the higher spatial wavenumbers, those beyond the lateral resolution of the lateral derivative method (LDM). Thus, given the stacking velocities and the zero‐offset traveltime to a given event as a function of midpoint, the LDM provides an estimate of the true vertical rms velocity to that event with a lateral resolution of about two mute zones or cable lengths. The LDM is applicable when lateral variations of velocity greater than 2 percent occur over the mute zone. At variations of 30 percent or greater, the internal assumptions of the LDM begin to break down. Synthetic models designed to test the LDM when the different assumptions are violated show that, in all cases, the results are not seriously affected. A test of the LDM on field data having a lateral velocity variation caused by sea floor topography gives a result which is supported by depth migration.

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Vehicle Velocity Estimation Fusion with Kinematic Integral and Empirical Correction on Multi-Timescales

Energies ◽

10.3390/en12071242 ◽

2019 ◽

Vol 12 (7) ◽

pp. 1242

Author(s):

Jiangyi Lv ◽

Hongwen He ◽

Wei Liu ◽

Yong Chen ◽

Fengchun Sun

Keyword(s):

Autonomous Vehicles ◽

Estimation Error ◽

Longitudinal Velocity ◽

Estimation Method ◽

Velocity Estimation ◽

Model Parameters ◽

Lateral Velocity ◽

Empirical Correction ◽

Vehicle Velocity ◽

Wide Range

Accurate and reliable vehicle velocity estimation is greatly motivated by the increasing demands of high-precision motion control for autonomous vehicles and the decreasing cost of the required multi-axis IMU sensors. A practical estimation method for the longitudinal and lateral velocities of electric vehicles is proposed. Two reliable driving empirical judgements about the velocities are extracted from the signals of the ordinary onboard vehicle sensors, which correct the integral errors of the corresponding kinematic equations on a long timescale. Meanwhile, the additive biases of the measured accelerations are estimated recursively by comparing the integral of the measured accelerations with the difference of the estimated velocities between the adjacent strong empirical correction instants, which further compensates the kinematic integral error on short timescale. The algorithm is verified by both the CarSim-Simulink co-simulation and the controller-in-the-loop test under the CarMaker-RoadBox environment. The results show that the velocities can be accurately and reliably estimated under a wide range of driving conditions without prior knowledge of the tire-model and other unavailable signals or frequently changeable model parameters. The relative estimation error of the longitudinal velocity and the absolute estimation error of the lateral velocity are kept within 2% and 0.5 km/h, respectively.

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Vehicle-trailer lateral velocity estimation using constrained unscented transformation

Vehicle System Dynamics ◽

10.1080/00423114.2020.1849745 ◽

2020 ◽

pp. 1-28

Author(s):

Amin Habibnejad Korayem ◽

Amir Khajepour ◽

Baris Fidan

Keyword(s):

Velocity Estimation ◽

Lateral Velocity ◽

Unscented Transformation

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Uncertainty comparison of velocity estimators for ultrasound flow imaging in narrow channels / Messunsicherheitsvergleich von Geschwindigkeitsschätzern der Ultraschallströmungsbildgebung in schmalen Kanälen

tm - Technisches Messen ◽

10.1515/teme-2019-0061 ◽

2019 ◽

Vol 86 (s1) ◽

pp. 7-11

Author(s):

David Weik ◽

Christian Kupsch ◽

Richard Nauber ◽

Lars Büttner ◽

Jürgen Czarske

Keyword(s):

Measurement Uncertainty ◽

Liquid Metals ◽

Ultrasound Image ◽

Velocity Estimation ◽

Estimation Methods ◽

Flow Imaging ◽

Lateral Velocity ◽

Narrow Channels ◽

Image Velocimetry ◽

Future Work

AbstractUltrasound Imaging with a linear phased array allows measuring turbulent vector profiles in two dimension with two components (2D2C). This is interesting in narrow channels for the application in battery cells or research in magnetohydrodynamics (MHD), where the access to the opaque fluid is often restricted. There are two main velocity estimation methods applicable: the Ultrasound Doppler Velocimetry (UDV) or the Ultrasound Image Velocimetry (UIV). In this work, these methods were evaluated by their attainable measurement uncertainty for the application in narrow channels, where the acquisition of the lateral velocity component is crucial. With a calibration rig, UIV could achieve a total measurement uncertainty of 9.9% and UDV 17.6%. As UIV reaches a 44% lower measurement uncertainty, it is the preferential method to measure lateral flows in narrow channels. In future work, the calibration rig will be adapted to optimize and characterize the flow instrumentation in opaque liquid metals.

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