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Recursive Identification of Cornering Stiffness Parameters for an Enhanced Single Track Model
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2009 (English)In: Proceedings of the 15th IFAC Symposiumon System Identification, 2009, 1726-1731 p.Conference paper (Refereed)
Abstract [en]

The current development of safety systems within the automotive industry heavily relies on the ability to perceive the environment. This is accomplished by using measurements from several different sensors within a sensor fusion framework. One important part of any system of this kind is an accurate model describing the motion of the vehicle. The most commonly used model for the lateral dynamics is the single track model, which includes the so called cornering stiffness parameters. These parameters describe the tire-road contact and are unknown and even time-varying. Hence, in order to fully make use of the single track model, these parameters have to be identified. The aim of this work is to provide a method for recursive identification of the cornering stiffness parameters to be used on-line while driving.

Place, publisher, year, edition, pages
2009. 1726-1731 p.
LiTH-ISY-R, ISSN 1400-3902 ; 2893
Keyword [en]
Recursive estimation, Recursive least square, Vehicle dynamics, Gray box model, Tire-road interaction
National Category
Engineering and Technology Control Engineering
URN: urn:nbn:se:liu:diva-45372DOI: 10.3182/20090706-3-FR-2004.00287Local ID: 82231ISBN: 978-3-902661-47-0OAI: diva2:266234
15th IFAC Symposium on System Identification, Saint-Malo, France, July, 2009
Available from: 2011-12-16 Created: 2009-10-10 Last updated: 2013-02-20Bibliographically approved
In thesis
1. Automotive Sensor Fusion for Situation Awareness
Open this publication in new window or tab >>Automotive Sensor Fusion for Situation Awareness
2009 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The use of radar and camera for situation awareness is gaining popularity in automotivesafety applications. In this thesis situation awareness consists of accurate estimates of theego vehicle’s motion, the position of the other vehicles and the road geometry. By fusinginformation from different types of sensors, such as radar, camera and inertial sensor, theaccuracy and robustness of those estimates can be increased.

Sensor fusion is the process of using information from several different sensors tocompute an estimate of the state of a dynamic system, that in some sense is better thanit would be if the sensors were used individually. Furthermore, the resulting estimate isin some cases only obtainable through the use of data from different types of sensors. Asystematic approach to handle sensor fusion problems is provided by model based stateestimation theory. The systems discussed in this thesis are primarily dynamic and they aremodeled using state space models. A measurement model is used to describe the relationbetween the state variables and the measurements from the different sensors. Within thestate estimation framework a process model is used to describe how the state variablespropagate in time. These two models are of major importance for the resulting stateestimate and are therefore given much attention in this thesis. One example of a processmodel is the single track vehicle model, which is used to model the ego vehicle’s motion.In this thesis it is shown how the estimate of the road geometry obtained directly from thecamera information can be improved by fusing it with the estimates of the other vehicles’positions on the road and the estimate of the radius of the ego vehicle’s currently drivenpath.

The positions of stationary objects, such as guardrails, lampposts and delineators aremeasured by the radar. These measurements can be used to estimate the border of theroad. Three conceptually different methods to represent and derive the road borders arepresented in this thesis. Occupancy grid mapping discretizes the map surrounding theego vehicle and the probability of occupancy is estimated for each grid cell. The secondmethod applies a constrained quadratic program in order to estimate the road borders,which are represented by two polynomials. The third method associates the radar measurementsto extended stationary objects and tracks them as extended targets.

The approaches presented in this thesis have all been evaluated on real data from bothfreeways and rural roads in Sweden.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2009. 76 p.
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1422
National Category
Information Science
urn:nbn:se:liu:diva-51226 (URN)LiU-TEK-LIC-2009:30 (Local ID)978-91-7393-492-3 (ISBN)LiU-TEK-LIC-2009:30 (Archive number)LiU-TEK-LIC-2009:30 (OAI)
2009-11-20, Visionen, B-building, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Available from: 2009-10-23 Created: 2009-10-22 Last updated: 2009-10-23Bibliographically approved

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