Definition of the Subject
The principal concerns in driving safety with standard vehicles are understanding and preventing risky situations. A close examination of accident data reveals that losing the vehicle control is the main reason for most car accidents. To help the driver to prevent such accidents, vehicle control systems may be used. For their optimal operation, these control systems require certain input data concerning vehicle dynamic parameters and vehicle–road interaction . Unfortunately, some fundamental parameters like the tire-road forces and the sideslip angle are difficult to measure in a car, for both technical and economic reasons. To face this problem, this study presents a dynamic modeling and observation method to estimate these variables. The ability to accurately estimate lateral tire forces and sideslip angle is a critical determinant...
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Abbreviations
- Lateral tire forces:
-
They are responsible to hold on the vehicle during a turn.
- Longitudinal tire forces:
-
They are responsible to accelerate/brake the vehicle.
- Observer or estimator:
-
It models a real system in order to provide an estimate of its internal state, given measurements of the input and output of the real system.
- Sideslip angle:
-
It is the angle between the velocity heading and the true heading of the vehicle.
- Tire forces:
-
The developed forces (longitudinal and lateral) are function of tire properties (material, tread pattern, tread depth, profile, etc.), the normal load on the tire, and the velocities experienced by the tire.
- Vehicle control systems:
-
They provide commands and instructions to control the movements of the vehicle in order to maintain stability and enhance passengers security and comfort.
- Vehicle dynamics:
-
It includes analytical and experimental technology used to study and understand the dynamical responses of a vehicle in various in-motion situations.
- Vertical (normal) tire forces:
-
They are responsible to support the weight of the vehicle.
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Acknowledgments
This work was done at Heudiasyc Laboratory UMR CNRS 6599, UTC University (Compiègne, France) in collaboration with Alessandro Victorino and Ali Charara.
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Doumiati, M., Lechner, D. (2012). Unscented Kalman Filter in Intelligent Vehicles . In: Meyers, R.A. (eds) Encyclopedia of Sustainability Science and Technology. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-0851-3_791
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DOI: https://doi.org/10.1007/978-1-4419-0851-3_791
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