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Road Intensity Based Mapping using Radar Measurements with a Probability Hypothesis Density Filter
Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan. (Sensor Fusion)
Volvo Car Corporation, Sweden. (Active Safety Electronics)
Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan. (Sensor Fusion)
2011 (engelsk)Inngår i: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 59, nr 4, s. 1397-1408Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Mapping stationary objects is essential for autonomous vehicles and many autonomous functions in vehicles. In this contribution the probability hypothesis density (PHD) filter framework is applied to automotive imagery sensor data for constructing such a map, where the main advantages are that it avoids the detection, the data association and the track handling problems in conventional multiple-target tracking, and that it gives a parsimonious representation of the map in contrast to grid based methods. Two original contributions address the inherent complexity issues of the algorithm: First, a data clustering algorithm is suggested to group the components of the PHD into different clusters, which structures the description of the prior and considerably improves the measurement update in the PHD filter. Second, a merging step is proposed to simplify the map representation in the PHD filter. The algorithm is applied to multi-sensor radar data collected on public roads, and the resulting map is shown to well describe the environment as a human perceives it.

sted, utgiver, år, opplag, sider
IEEE Signal Processing Society, 2011. Vol. 59, nr 4, s. 1397-1408
Emneord [en]
Clustering, Gaussian mixture, PHD, mapping, probability hypothesis density, road edge estimation
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-66449DOI: 10.1109/TSP.2010.2103065ISI: 000290810100006OAI: oai:DiVA.org:liu-66449DiVA, id: diva2:404125
Prosjekter
IVSS - SEFSCADICS
Merknad

©2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Tilgjengelig fra: 2011-03-24 Laget: 2011-03-16 Sist oppdatert: 2017-12-11bibliografisk kontrollert
Inngår i avhandling
1. Sensor Fusion for Automotive Applications
Åpne denne publikasjonen i ny fane eller vindu >>Sensor Fusion for Automotive Applications
2011 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Linköping: Linköping University Electronic Press, 2011. s. 93
Serie
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1409
Emneord
Kalman filter, PHD filter, extended targets, tracking, sensor fusion, road model, single track model, bicycle model
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-71594 (URN)978-91-7393-023-9 (ISBN)
Disputas
2011-11-25, Key 1, Hus Key, Campus Valla, Linköpings universitet, Linköping, 13:15 (engelsk)
Opponent
Veileder
Prosjekter
SEFS -- IVSSVR - ETT
Tilgjengelig fra: 2011-10-26 Laget: 2011-10-24 Sist oppdatert: 2019-12-19bibliografisk kontrollert

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