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Tire Radii and Vehicle Trajectory Estimation Using a Marginalized Particle Filter
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.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Measurements of individual wheel speeds and absolute position from a global navigation satellite system (gnss) are used for high-precision estimation of vehicle tire radii in this work. The radii deviation from its nominal value is modeled as a Gaussian process and included as noise components in a vehicle model. The novelty lies in a Bayesian approach to estimate online both the state vector of the vehicle model and noise parameters using a marginalized particle filter. No model approximations are needed such as in previously proposed algorithms based on the extended Kalman filter. The proposed approach outperforms common methods used for joint state and parameter estimation when compared with respect to accuracy and computational time. Field tests show that the absolute radius can be estimated with millimeter accuracy, while the relative wheel radius on one axle is estimated with submillimeter accuracy.

National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-71864OAI: diva2:454783
Available from: 2011-11-08 Created: 2011-11-08 Last updated: 2011-11-08Bibliographically approved
In thesis
1. Sensor Fusion for Automotive Applications
Open this publication in new window or tab >>Sensor Fusion for Automotive Applications
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Mapping stationary objects and tracking moving targets are essential for many autonomous functions in vehicles. In order to compute the map and track estimates, sensor measurements from radar, laser and camera are used together with the standard proprioceptive sensors present in a car. By fusing information from different types of sensors, the accuracy and robustness of the estimates can be increased.

Different types of maps are discussed and compared in the thesis. In particular, road maps make use of the fact that roads are highly structured, which allows relatively simple and powerful models to be employed. It is shown how the information of the lane markings, obtained by a front looking camera, can be fused with inertial measurement of the vehicle motion and radar measurements of vehicles ahead to compute a more accurate and robust road geometry estimate. Further, it is shown how radar measurements of stationary targets can be used to estimate the road edges, modeled as polynomials and tracked as extended targets.

Recent advances in the field of multiple target tracking lead to the use of finite set statistics (FISST) in a set theoretic approach, where the targets and the measurements are treated as random finite sets (RFS). The first order moment of a RFS is called probability hypothesis density (PHD), and it is propagated in time with a PHD filter. In this thesis, the PHD filter is applied to radar data for constructing a parsimonious representation of the map of the stationary objects around the vehicle. Two original contributions, which exploit the inherent structure in the map, are proposed. A data clustering algorithm is suggested to structure the description of the prior and considerably improving the update in the PHD filter. Improvements in the merging step further simplify the map representation.

When it comes to tracking moving targets, the focus of this thesis is on extended targets, i.e., targets which potentially may give rise to more than one measurement per time step. An implementation of the PHD filter, which was proposed to handle data obtained from extended targets, is presented. An approximation is proposed in order to limit the number of hypotheses. Further, a framework to track the size and shape of a target is introduced. The method is based on measurement generating points on the surface of the target, which are modeled by an RFS.

Finally, an efficient and novel Bayesian method is proposed for approximating the tire radii of a vehicle based on particle filters and the marginalization concept. This is done under the assumption that a change in the tire radius is caused by a change in tire pressure, thus obtaining an indirect tire pressure monitoring system.

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

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. 93 p.
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1409
Kalman filter, PHD filter, extended targets, tracking, sensor fusion, road model, single track model, bicycle model
National Category
Signal Processing
urn:nbn:se:liu:diva-71594 (URN)978-91-7393-023-9 (ISBN)
Public defence
2011-11-25, Key 1, Hus Key, Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Available from: 2011-10-26 Created: 2011-10-24 Last updated: 2011-11-08Bibliographically approved

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Lundquist, ChristianÖzkan, EmreGustafsson, Fredrik
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