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Sensor Fusion for Automotive Applications
Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan. (Sensor Fusion)
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 [en]
Kalman filter, PHD filter, extended targets, tracking, sensor fusion, road model, single track model, bicycle model
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-71594ISBN: 978-91-7393-023-9 (tryckt)OAI: oai:DiVA.org:liu-71594DiVA, id: diva2:451021
Disputas
2011-11-25, Key 1, Hus Key, Campus Valla, Linköpings universitet, Linköping, 13:15 (engelsk)
Opponent
Veileder
Prosjekter
SEFS -- IVSSVR - ETTTilgjengelig fra: 2011-10-26 Laget: 2011-10-24 Sist oppdatert: 2019-12-19bibliografisk kontrollert
Delarbeid
1. Situational Awareness and Road Prediction for Trajectory Control Applications
Åpne denne publikasjonen i ny fane eller vindu >>Situational Awareness and Road Prediction for Trajectory Control Applications
2012 (engelsk)Inngår i: Handbook of Intelligent Vehicles / [ed] Azim Eskandarian, Springer London, 2012, s. 365-396Kapittel i bok, del av antologi (Fagfellevurdert)
Abstract [en]

The Handbook of Intelligent Vehicles provides a complete coverage of the fundamentals, new technologies, and sub-areas essential to the development of intelligent vehicles; it also includes advances made to date, challenges, and future trends. Significant strides in the field have been made to date; however, so far there has been no single book or volume which captures these advances in a comprehensive format, addressing all essential components and subspecialties of intelligent vehicles, as this book does. Since the intended users are engineering practitioners, as well as researchers and graduate students, the book chapters do not only cover fundamentals, methods, and algorithms but also include how software/hardware are implemented, and demonstrate the advances along with their present challenges. Research at both component and systems levels are required to advance the functionality of intelligent vehicles. This volume covers both of these aspects in addition to the fundamentals listed above.

sted, utgiver, år, opplag, sider
Springer London, 2012
Emneord
Engineering, Artificial intelligence, Automotive Engineering, Control, Robotics, Mechatronics
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-71660 (URN)10.1007/978-0-85729-085-4_15 (DOI)978-0-85729-084-7 (ISBN)978-0-85729-085-4 (ISBN)
Forskningsfinansiär
Swedish Research CouncilSwedish Foundation for Strategic Research
Tilgjengelig fra: 2011-11-08 Laget: 2011-10-27 Sist oppdatert: 2014-11-28bibliografisk kontrollert
2. Joint Ego-Motion and Road Geometry Estimation
Åpne denne publikasjonen i ny fane eller vindu >>Joint Ego-Motion and Road Geometry Estimation
2011 (engelsk)Inngår i: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 12, nr 4, s. 253-263Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

We provide a sensor fusion framework for solving the problem of joint egomotion and road geometry estimation. More specifically we employ a sensor fusion framework to make systematic use of the measurements from a forward looking radar and camera, steering wheel angle sensor, wheel speed sensors and inertial sensors to compute good estimates of the road geometry and the motion of the ego vehicle on this road. In order to solve this problem we derive dynamical models for the ego vehicle, the road and the leading vehicles. The main difference to existing approaches is that we make use of a new dynamic model for the road. An extended Kalman filter is used to fuse data and to filter measurements from the camera in order to improve the road geometry estimate. The proposed solution has been tested and compared to existing algorithms for this problem, using measurements from authentic traffic environments on public roads in Sweden. The results clearly indicate that the proposed method provides better estimates.

sted, utgiver, år, opplag, sider
Elsevier, 2011
Emneord
Sensor fusion, Single track model, Bicycle model, Road geometry estimation, Extended Kalman filter
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-51243 (URN)10.1016/j.inffus.2010.06.007 (DOI)000293207500004 ()
Prosjekter
IVSS - SEFS
Tilgjengelig fra: 2011-01-13 Laget: 2009-10-23 Sist oppdatert: 2017-12-12bibliografisk kontrollert
3. Extended Target Tracking Using Polynomials With Applications to Road-Map Estimation
Åpne denne publikasjonen i ny fane eller vindu >>Extended Target Tracking Using Polynomials With Applications to Road-Map Estimation
2011 (engelsk)Inngår i: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 59, nr 1, s. 15-26Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

This paper presents an extended target tracking framework which uses polynomials in order to model extended objects in the scene of interest from imagery sensor data. State-space models are proposed for the extended objects which enables the use of Kalman filters in tracking. Different methodologies of designing measurement equations are investigated. A general target tracking algorithm that utilizes a specific data association method for the extended targets is presented. The overall algorithm must always use some form of prior information in order to detect and initialize extended tracks from the point tracks in the scene. This aspect of the problem is illustrated on a real life example of road-map estimation from automotive radar reports along with the results of the study.

sted, utgiver, år, opplag, sider
IEEE Signal Processing Society, 2011
Emneord
Automotive radar, EIV, Data association, Errors in output, Errors in variables, Extended target tracking, Parabola, Polynomial, Road map
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-63831 (URN)10.1109/TSP.2010.2081983 (DOI)000285519200002 ()
Prosjekter
IVSS - SEFSSSF - MOVIII
Forskningsfinansiär
Swedish Foundation for Strategic Research
Tilgjengelig fra: 2011-01-13 Laget: 2011-01-04 Sist oppdatert: 2017-12-11bibliografisk kontrollert
4. Road Intensity Based Mapping using Radar Measurements with a Probability Hypothesis Density Filter
Åpne denne publikasjonen i ny fane eller vindu >>Road Intensity Based Mapping using Radar Measurements with a Probability Hypothesis Density Filter
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
Emneord
Clustering, Gaussian mixture, PHD, mapping, probability hypothesis density, road edge estimation
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-66449 (URN)10.1109/TSP.2010.2103065 (DOI)000290810100006 ()
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
5. Extended Target Tracking Using a Gaussian-Mixture PHD Filter
Åpne denne publikasjonen i ny fane eller vindu >>Extended Target Tracking Using a Gaussian-Mixture PHD Filter
2012 (engelsk)Inngår i: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 48, nr 4, s. 3268-3286Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

This paper presents a Gaussian-mixture implementation of the phd filter for tracking extended targets. The exact filter requires processing of all possible measurement set partitions, which is generally infeasible to implement. A method is proposed for limiting the number of considered partitions and possible alternatives are discussed. The implementation is used on simulated data and in experiments with real laser data, and the advantage of the filter is illustrated. Suitable remedies are given to handle spatially close targets and target occlusion.

Emneord
Target tracking, Extended target, PHD filter, Random set, Gaussian-mixture, Laser sensor
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-71866 (URN)10.1109/TAES.2012.6324703 (DOI)000309865600030 ()
Prosjekter
CADICSETTCUAS
Forskningsfinansiär
Swedish Foundation for Strategic Research Swedish Research Council
Tilgjengelig fra: 2012-10-01 Laget: 2011-11-08 Sist oppdatert: 2017-12-08bibliografisk kontrollert
6. Estimating the Shape of Targets with a PHD Filter
Åpne denne publikasjonen i ny fane eller vindu >>Estimating the Shape of Targets with a PHD Filter
2011 (engelsk)Inngår i: Proceedings of the 14th International Conference on Information Fusion, 2011Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

This paper presents a framework for tracking extended targets which give rise to a structured set of measurements per each scan. The concept of a measurement generating point (MGP) which is defined on the boundary of each target is introduced. The tracking framework contains an hybrid statespace where MGP:s and the measurements are modeled by random finite sets and target states by random vectors. The target states are assumed to be partitioned into linear and nonlinear components and a Rao-Blackwellized particle filter is used for their estimation. For each state particle, a probability hypothesis density (PHD) filter is utilized for estimating the conditional set of MGP:s given the target states. The PHD kept for each particle serves as a useful means to represent information in the set of measurements about the target states. The early results obtained show promising performance with stable target following capability and reasonable shape estimates.

Emneord
Tracking, Data association, Particle filter, Kalman filter, Estimation, PHD filter, Extended target, Rao-Blackwellized particle filter
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-69945 (URN)978-1-4577-0267-9 (ISBN)
Konferanse
14th International Conference on Information Fusion, 5-8 July, Chicago, Illinois, USA
Prosjekter
CADICS
Tilgjengelig fra: 2011-08-12 Laget: 2011-08-09 Sist oppdatert: 2014-03-27bibliografisk kontrollert
7. Tire Radii and Vehicle Trajectory Estimation Using a Marginalized Particle Filter
Åpne denne publikasjonen i ny fane eller vindu >>Tire Radii and Vehicle Trajectory Estimation Using a Marginalized Particle Filter
(engelsk)Manuskript (preprint) (Annet vitenskapelig)
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.

HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-71864 (URN)
Tilgjengelig fra: 2011-11-08 Laget: 2011-11-08 Sist oppdatert: 2011-11-08bibliografisk kontrollert

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