Mixed Kinematics and Camera Based Vehicle Dynamic Sideslip Estimation for an RC Scaled Model

Kuyt, Conno; Como, Matteo

doi:10.1109/ccta.2018.8511487

Cited by 6 publications

(1 citation statement)

References 26 publications

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“…With the advent of more accurate sensing devices on vehicles, new approaches to SSA estimation, known as vision-based observers, have emerged. These approaches use cameras and image processing algorithms to estimate the sideslip angle [56][57][58]. They require visual features such as road markings or landmarks to be visible in the camera images, without which the estimation may result in failure.…”

Section: The Recent Work On Estimating Vehicle Sideslip Anglementioning

confidence: 99%

Robust Virtual Sensing of the Vehicle Sideslip Angle through the Cross-Combination of Multiple Filters Using a Decision Tree Algorithm

Atheupe,

El Mrhasli,

Emabou

et al. 2023

Sensors

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This paper presents a state-of-the-art estimation technique by cross-combining a number n of filters for high-precision, reliable and robust vehicle sideslip angle state estimation, over a full range of vehicle operations irrespective of the driving mission and disruptions that may occur in the system. A machine-learning algorithm based on decision trees connects several filters together to switch between them according to the driving context, ensuring the best possible state estimate for relatively small and large sideslip angle values. In conjunction with the above-mentioned aspects, a seamless transition between different vehicle models is attained by observing the key parameters characterizing the lateral motion of the vehicle. The tests conducted using a prototype vehicle on a snow-covered track confirm the effectiveness and reliability of the proposed approach.

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Section: The Recent Work On Estimating Vehicle Sideslip Anglementioning

confidence: 99%