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  • 1.
    Ardeshiri, Tohid
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Orguner, Umut
    Middle East Technical University.
    Lundquist, Christian
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Schön, Thomas
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    On mixture reduction for multiple target tracking2012Konferensbidrag (Refereegranskat)
  • 2.
    Granström, Karl
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Lundquist, Christian
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    On the Use of Multiple Measurement Models for Extended Target Tracking2013Ingår i: Proceedings of the 16th International Conference on Information Fusion, 2013, s. 1534-1541Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper considers extended targets that have constant extension shapes, but generate measurements whose appearance can change abruptly. The problem is approached using multiple measurement models, where each model corresponds to a measurement appearance mode. Mode transitions are modeled as dependent on the extended target kinematic state, and a multiple model extended target PHD filter is used to handle multiple targets with multiple appearance modes. The extended target tracking is evaluated using real world data where a laser range sensor is used to track multiple bicycles.

  • 3.
    Granström, Karl
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Lundquist, Christian
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Orguner, Umut
    Department of Electrical and Electronics Engineering, Middle East Technical University.
    Random Set Methods: Estimation of Multiple Extended Objects2014Ingår i: IEEE robotics & automation magazine, ISSN 1070-9932, E-ISSN 1558-223X, Vol. 21, nr 2, s. 73-82Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Random set based methods have provided a rigorous Bayesian framework and have been used extensively in the last decade for point object estimation. In this paper, we emphasize that the same methodology offers an equally powerful approach to estimation of so called extended objects, i.e., objects that result in multiple detections on the sensor side. Building upon the analogy between Bayesian state estimation of a single object and random finite set estimation for multiple objects, we give a tutorial on random set methods with an emphasis on multiple extended object estimation. The capabilities are illustrated on a simple yet insightful real life example with laser range data containing several occlusions.

  • 4.
    Granström, Karl
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Lundquist, Christian
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Orguner, Umut
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    A Gaussian Mixture PHD Filter for Extended Target Tracking2010Rapport (Övrigt vetenskapligt)
    Abstract [en]

    In extended target tracking, targets potentially produce more than one measurement per time step. Multiple extended targets are therefore usually hard to track, due to the resulting complex data association. The main contribution of this paper is the implementation of a Probability Hypothesis Density (PHD) filter for tracking of multiple extended targets. A general modification of the PHD filter to handle extended targets has been presented recently by Mahler, and the novelty in this work lies in the realisation of a Gaussian mixture PHD filter for extended targets. Furthermore, we propose a method to easily partition the measurements into a number of subsets, each of which is supposed to contain measurements that all stem from the same source. The method is illustrated in simulation examples, and the advantage of the implemented extended target PHD filter is shown in a comparison with a standard PHD filter.

  • 5.
    Granström, Karl
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Lundquist, Christian
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Orguner, Umut
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    A Gaussian Mixture PHD Filter for Extended Target Tracking2010Ingår i: Proceedings of the 13th International Conference on Information Fusion, 2010Konferensbidrag (Refereegranskat)
    Abstract [en]

    In extended target tracking, targets potentially produce more than one measurement per time step. Multiple extended targets are therefore usually hard to track, due to the resulting complex data association. The main contribution of this paper is the implementation of a Probability Hypothesis Density (PHD) filter for tracking of multiple extended targets. A general modification of the PHD filter to handle extended targets has been presented recently by Mahler, and the novelty in this work lies in the realisation of a Gaussian mixture PHD filter for extended targets. Furthermore, we propose a method to easily partition the measurements into a number of subsets, each of which is supposed to contain measurements that all stem from the same source. The method is illustrated in simulation examples, and the advantage of the implemented extended target PHD filter is shown in a comparison with a standard PHD filter.

  • 6.
    Granström, Karl
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Lundquist, Christian
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Orguner, Umut
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Extended Target Tracking using a Gaussian-Mixture PHD Filter2011Rapport (Övrigt vetenskapligt)
    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.

  • 7.
    Granström, Karl
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Lundquist, Christian
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Orguner, Umut
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Extended Target Tracking Using a Gaussian-Mixture PHD Filter2012Ingår i: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 48, nr 4, s. 3268-3286Artikel i tidskrift (Refereegranskat)
    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.

  • 8.
    Granström, Karl
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Lundquist, Christian
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Orguner, Umut
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Tracking Rectangular and Elliptical Extended Targets Using Laser Measurements2011Ingår i: Proceedings of the 14th International Conference on Information Fusion, 2011, s. 592-599Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper considers tracking of extended targets using data from laser range sensors. Two types of extended target shapes are considered, rectangular and elliptical, and a method to compute predicted measurements and corresponding innovation covariances is suggested. The proposed method can easily be integrated into any tracking framework that relies on the use of an extended Kalman filter. Here, it is used together with a recently proposed Gaussian mixture probability hypothesis density (GM-PHD) filter for extended target tracking, which enables estimation of not only position, orientation, and size of the extended targets, but also estimation of extended target type (i.e. rectangular or elliptical). In both simulations and experiments using laser data, the versatility of the proposed tracking framework is shown. In addition, a simple measure to evaluate the extended target tracking results is suggested.

  • 9.
    Lundquist, Christian
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Automotive Sensor Fusion for Situation Awareness2009Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
    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.

    Delarbeten
    1. Joint Ego-Motion and Road Geometry Estimation
    Öppna denna publikation i ny flik eller fönster >>Joint Ego-Motion and Road Geometry Estimation
    2011 (Engelska)Ingår i: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 12, nr 4, s. 253-263Artikel i tidskrift (Refereegranskat) 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.

    Ort, förlag, år, upplaga, sidor
    Elsevier, 2011
    Nyckelord
    Sensor fusion, Single track model, Bicycle model, Road geometry estimation, Extended Kalman filter
    Nationell ämneskategori
    Signalbehandling Reglerteknik
    Identifikatorer
    urn:nbn:se:liu:diva-51243 (URN)10.1016/j.inffus.2010.06.007 (DOI)000293207500004 ()
    Projekt
    IVSS - SEFS
    Tillgänglig från: 2011-01-13 Skapad: 2009-10-23 Senast uppdaterad: 2017-12-12Bibliografiskt granskad
    2. Recursive Identification of Cornering Stiffness Parameters for an Enhanced Single Track Model
    Öppna denna publikation i ny flik eller fönster >>Recursive Identification of Cornering Stiffness Parameters for an Enhanced Single Track Model
    2009 (Engelska)Ingår i: Proceedings of the 15th IFAC Symposiumon System Identification, 2009, s. 1726-1731Konferensbidrag, Publicerat paper (Refereegranskat)
    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.

    Serie
    LiTH-ISY-R, ISSN 1400-3902 ; 2893
    Nyckelord
    Recursive estimation, Recursive least square, Vehicle dynamics, Gray box model, Tire-road interaction
    Nationell ämneskategori
    Teknik och teknologier Reglerteknik
    Identifikatorer
    urn:nbn:se:liu:diva-45372 (URN)10.3182/20090706-3-FR-2004.00287 (DOI)82231 (Lokalt ID)978-3-902661-47-0 (ISBN)82231 (Arkivnummer)82231 (OAI)
    Konferens
    15th IFAC Symposium on System Identification, Saint-Malo, France, July, 2009
    Tillgänglig från: 2011-12-16 Skapad: 2009-10-10 Senast uppdaterad: 2013-02-20Bibliografiskt granskad
    3. Estimation of the Free Space in Front of a Moving Vehicle
    Öppna denna publikation i ny flik eller fönster >>Estimation of the Free Space in Front of a Moving Vehicle
    2009 (Engelska)Ingår i: Proceedings of the '09 SAE World Congress & Exhibition, 2009Konferensbidrag, Publicerat paper (Refereegranskat)
    Abstract [en]

    There are more and more systems emerging making use of measurements from a forward looking radar and a forward looking camera. It is by now well known how to exploit this data in order to compute estimates of the road geometry, tracking leading vehicles, etc. However, there is valuable information present in the radar concerning stationary objects, that is typically not used. The present work shows how radar measurements of stationary objects can be used to obtain a reliable estimate of the free space in front of a moving vehicle. The approach has been evaluated on real data from highways and rural roads in Sweden.

    Nyckelord
    Road geometry, Weighted least squares, Quadratic program, Road borders, Free space estimation, Automotive radar, Road mapping
    Nationell ämneskategori
    Teknik och teknologier Reglerteknik
    Identifikatorer
    urn:nbn:se:liu:diva-44998 (URN)10.4271/2009-01-1288 (DOI)79290 (Lokalt ID)978-0-7680-2126-4 (ISBN)79290 (Arkivnummer)79290 (OAI)
    Konferens
    SAE World Congress & Exhibition, April 2009, Detroit, MI, USA
    Tillgänglig från: 2011-12-16 Skapad: 2009-10-10 Senast uppdaterad: 2013-02-21Bibliografiskt granskad
    4. Tracking Stationary Extended Objects for Road Mapping using Radar Measurements
    Öppna denna publikation i ny flik eller fönster >>Tracking Stationary Extended Objects for Road Mapping using Radar Measurements
    2009 (Engelska)Ingår i: Proceedings of the '09 IEEE Intelligent Vehicle Symposium, IEEE , 2009, s. 405-410Konferensbidrag, Publicerat paper (Refereegranskat)
    Abstract [en]

    It is getting more common that premium cars areequipped with a forward looking radar and a forward looking camera. The data is often used to estimate the road geometry, tracking leading vehicles, etc. However, there is valuable information present in the radar concerning stationary objects, that is typically not used. The present work shows how stationary objects, such as guard rails, can be modeled and tracked as extended objects using radar measurements. The problem is cast within a standard sensor fusion framework utilizing the Kalman filter. The approach has been evaluated on real datafrom highways and rural roads in Sweden.

    Ort, förlag, år, upplaga, sidor
    IEEE, 2009
    Serie
    IEEE Intelligent Vehicles Symposium. Proceedings, ISSN 1931-0587
    Nyckelord
    Extended objects, Object detection, Radar imaging, Road vehicle radar, Object tracking, Road mapping, Stationary objects
    Nationell ämneskategori
    Teknik och teknologier Reglerteknik
    Identifikatorer
    urn:nbn:se:liu:diva-18179 (URN)10.1109/IVS.2009.5164312 (DOI)978-1-4244-3504-3 (ISBN)978-1-4244-3503-6 (ISBN)
    Konferens
    '09 IEEE Intelligent Vehicle Symposium, Xi’an, China, June, 2009
    Projekt
    IVSS - SEFSMOVIII
    Tillgänglig från: 2011-08-12 Skapad: 2009-05-09 Senast uppdaterad: 2013-07-22Bibliografiskt granskad
  • 10.
    Lundquist, Christian
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Sensor Fusion for Automotive Applications2011Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    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.

    Delarbeten
    1. Situational Awareness and Road Prediction for Trajectory Control Applications
    Öppna denna publikation i ny flik eller fönster >>Situational Awareness and Road Prediction for Trajectory Control Applications
    2012 (Engelska)Ingår i: Handbook of Intelligent Vehicles / [ed] Azim Eskandarian, Springer London, 2012, s. 365-396Kapitel i bok, del av antologi (Refereegranskat)
    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.

    Ort, förlag, år, upplaga, sidor
    Springer London, 2012
    Nyckelord
    Engineering, Artificial intelligence, Automotive Engineering, Control, Robotics, Mechatronics
    Nationell ämneskategori
    Reglerteknik Signalbehandling
    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
    VetenskapsrådetStiftelsen för strategisk forskning (SSF)
    Tillgänglig från: 2011-11-08 Skapad: 2011-10-27 Senast uppdaterad: 2014-11-28Bibliografiskt granskad
    2. Joint Ego-Motion and Road Geometry Estimation
    Öppna denna publikation i ny flik eller fönster >>Joint Ego-Motion and Road Geometry Estimation
    2011 (Engelska)Ingår i: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 12, nr 4, s. 253-263Artikel i tidskrift (Refereegranskat) 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.

    Ort, förlag, år, upplaga, sidor
    Elsevier, 2011
    Nyckelord
    Sensor fusion, Single track model, Bicycle model, Road geometry estimation, Extended Kalman filter
    Nationell ämneskategori
    Signalbehandling Reglerteknik
    Identifikatorer
    urn:nbn:se:liu:diva-51243 (URN)10.1016/j.inffus.2010.06.007 (DOI)000293207500004 ()
    Projekt
    IVSS - SEFS
    Tillgänglig från: 2011-01-13 Skapad: 2009-10-23 Senast uppdaterad: 2017-12-12Bibliografiskt granskad
    3. Extended Target Tracking Using Polynomials With Applications to Road-Map Estimation
    Öppna denna publikation i ny flik eller fönster >>Extended Target Tracking Using Polynomials With Applications to Road-Map Estimation
    2011 (Engelska)Ingår i: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 59, nr 1, s. 15-26Artikel i tidskrift (Refereegranskat) 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.

    Ort, förlag, år, upplaga, sidor
    IEEE Signal Processing Society, 2011
    Nyckelord
    Automotive radar, EIV, Data association, Errors in output, Errors in variables, Extended target tracking, Parabola, Polynomial, Road map
    Nationell ämneskategori
    Signalbehandling Reglerteknik
    Identifikatorer
    urn:nbn:se:liu:diva-63831 (URN)10.1109/TSP.2010.2081983 (DOI)000285519200002 ()
    Projekt
    IVSS - SEFSSSF - MOVIII
    Forskningsfinansiär
    Stiftelsen för strategisk forskning (SSF)
    Tillgänglig från: 2011-01-13 Skapad: 2011-01-04 Senast uppdaterad: 2017-12-11Bibliografiskt granskad
    4. Road Intensity Based Mapping using Radar Measurements with a Probability Hypothesis Density Filter
    Öppna denna publikation i ny flik eller fönster >>Road Intensity Based Mapping using Radar Measurements with a Probability Hypothesis Density Filter
    2011 (Engelska)Ingår i: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 59, nr 4, s. 1397-1408Artikel i tidskrift (Refereegranskat) 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.

    Ort, förlag, år, upplaga, sidor
    IEEE Signal Processing Society, 2011
    Nyckelord
    Clustering, Gaussian mixture, PHD, mapping, probability hypothesis density, road edge estimation
    Nationell ämneskategori
    Signalbehandling Reglerteknik
    Identifikatorer
    urn:nbn:se:liu:diva-66449 (URN)10.1109/TSP.2010.2103065 (DOI)000290810100006 ()
    Projekt
    IVSS - SEFSCADICS
    Anmärkning

    ©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.

    Tillgänglig från: 2011-03-24 Skapad: 2011-03-16 Senast uppdaterad: 2017-12-11Bibliografiskt granskad
    5. Extended Target Tracking Using a Gaussian-Mixture PHD Filter
    Öppna denna publikation i ny flik eller fönster >>Extended Target Tracking Using a Gaussian-Mixture PHD Filter
    2012 (Engelska)Ingår i: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 48, nr 4, s. 3268-3286Artikel i tidskrift (Refereegranskat) 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.

    Nyckelord
    Target tracking, Extended target, PHD filter, Random set, Gaussian-mixture, Laser sensor
    Nationell ämneskategori
    Signalbehandling Reglerteknik
    Identifikatorer
    urn:nbn:se:liu:diva-71866 (URN)10.1109/TAES.2012.6324703 (DOI)000309865600030 ()
    Projekt
    CADICSETTCUAS
    Forskningsfinansiär
    Stiftelsen för strategisk forskning (SSF)Vetenskapsrådet
    Tillgänglig från: 2012-10-01 Skapad: 2011-11-08 Senast uppdaterad: 2017-12-08Bibliografiskt granskad
    6. Estimating the Shape of Targets with a PHD Filter
    Öppna denna publikation i ny flik eller fönster >>Estimating the Shape of Targets with a PHD Filter
    2011 (Engelska)Ingår i: Proceedings of the 14th International Conference on Information Fusion, 2011Konferensbidrag, Publicerat paper (Refereegranskat)
    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.

    Nyckelord
    Tracking, Data association, Particle filter, Kalman filter, Estimation, PHD filter, Extended target, Rao-Blackwellized particle filter
    Nationell ämneskategori
    Signalbehandling Reglerteknik
    Identifikatorer
    urn:nbn:se:liu:diva-69945 (URN)978-1-4577-0267-9 (ISBN)
    Konferens
    14th International Conference on Information Fusion, 5-8 July, Chicago, Illinois, USA
    Projekt
    CADICS
    Tillgänglig från: 2011-08-12 Skapad: 2011-08-09 Senast uppdaterad: 2014-03-27Bibliografiskt granskad
    7. Tire Radii and Vehicle Trajectory Estimation Using a Marginalized Particle Filter
    Öppna denna publikation i ny flik eller fönster >>Tire Radii and Vehicle Trajectory Estimation Using a Marginalized Particle Filter
    (Engelska)Manuskript (preprint) (Övrigt vetenskapligt)
    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.

    Nationell ämneskategori
    Teknik och teknologier
    Identifikatorer
    urn:nbn:se:liu:diva-71864 (URN)
    Tillgänglig från: 2011-11-08 Skapad: 2011-11-08 Senast uppdaterad: 2011-11-08Bibliografiskt granskad
  • 11.
    Lundquist, Christian
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Ahrholdt, Malte
    Volvo Technology AB, Sweden.
    Bengtsson, Fredrik
    Volvo 3P, Sweden.
    Danielsson, Lars
    Volvo Car Corporation, Sweden.
    SEFS – Results on Sensor Data Fusion System Development2009Ingår i: Proceedings of the 16th ITS World Congress, 2009, s. 3483-3490Konferensbidrag (Refereegranskat)
    Abstract [en]

    For driver assistance systems, a thorough perception of the environment becomes more and more important. Often, sensor data fusion systems (comprising typically sensors such as radar and vision systems) are employed, to get an improved picture of the host vehicle's surroundings. The SEFS project is part of the Swedish IVSS program. The project focuses on methods and architectures for fusion of sensor data. In this paper, some of the main results are highlighted. Findings of the SEFS project include a data fusion structure and architecture, tracking methods as well as vehicle and road models plus related parameter estimation.

  • 12.
    Lundquist, Christian
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Ahrholdt, Malte
    Volvo Technology AB, Sweden.
    Bengtsson, Fredrik
    Volvo 3P, Sweden.
    Danielsson, Lars
    Volvo Car Corporation, Sweden.
    SEFS – Results on Sensor Data Fusion System Development2009Rapport (Övrigt vetenskapligt)
    Abstract [en]

    For driver assistance systems, a thorough perception of the environment becomes more and more important. Often, sensor data fusion systems (comprising typically sensors such as radar and vision systems) are employed, to get an improved picture of the host vehicle's surroundings. The SEFS project is part of the Swedish IVSS program. The project focuses on methods and architectures for fusion of sensor data. In this paper, some of the main results are highlighted. Findings of the SEFS project include a data fusion structure and architecture, tracking methods as well as vehicle and road models plus related parameter estimation.

  • 13.
    Lundquist, Christian
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Danielsson, Lars
    Chalmers University of Technology, Sweden.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Random Set Based Road Mapping using Radar Measurements2010Rapport (Övrigt vetenskapligt)
    Abstract [en]

    This work is concerned with the problem of multi-sensor multi-target tracking of stationary road side objects, i.e. guard rails and parked vehicles, in the context of automotive active safety systems. Advanced active safety applications, such as collision avoidance by steering, rely on obtaining a detailed map of the surrounding infrastructure to accurately assess the situation. Here, this map consists of the position of objects, represented by a random finite set (RFS) of multi-target states and we propose to describe the map as the spatial stationary object intensity. This intensity is the first order moment of a multi-target RFS representing the position of stationary objects and it is calculated using a Gaussian mixture probability hypothesis density (GM-PHD) filter.

  • 14.
    Lundquist, Christian
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Danielsson, Lars
    Chalmers University of Technology, Sweden.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Random Set Based Road Mapping using Radar Measurements2010Ingår i: Proceedings of the 18th European Signal Processing Conference, 2010, s. 219-223Konferensbidrag (Refereegranskat)
    Abstract [en]

    This work is concerned with the problem of multi-sensor multi-target tracking of stationary road side objects, i.e. guard rails and parked vehicles, in the context of automotive active safety systems. Advanced active safety applications, such as collision avoidance by steering, rely on obtaining a detailed map of the surrounding infrastructure to accurately assess the situation. Here, this map consists of the position of objects, represented by a random finite set (RFS) of multi-target states and we propose to describe the map as the spatial stationary object intensity. This intensity is the first order moment of a multi-target RFS representing the position of stationary objects and it is calculated using a Gaussian mixture probability hypothesis density (GM-PHD) filter.

  • 15.
    Lundquist, Christian
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Granström, Karl
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Orguner, Umut
    Middle East Technical University, Ankara, Turkey.
    An Extended Target CPHD Filter and a Gamma Gaussian Inverse Wishart Implementation2013Ingår i: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 7, nr 3, s. 472-483Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper presents a cardinalized probability hypothesis density (CPHD) filter for extended targets that can result in multiple measurements at each scan. The probability hypothesis density (PHD) filter for such targets has been derived by Mahler, and different implementations have been proposed recently. To achieve better estimation performance this work relaxes the Poisson assumptions of the extended target PHD filter in target and measurement numbers. A gamma Gaussian inverse Wishart mixture implementation, which is capable of estimating the target extents and measurement rates as well as the kinematic state of the target, is proposed, and it is compared to its PHD counterpart in a simulation study. The results clearly show that the CPHD filter has a more robust cardinality estimate leading to smaller OSPA errors, which confirms that the extended target CPHD filter inherits the properties of its point target counterpart.

  • 16.
    Lundquist, Christian
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Granström, Karl
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Orguner, Umut
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Estimating the Shape of Targets with a PHD Filter2011Ingår i: Proceedings of the 14th International Conference on Information Fusion, 2011Konferensbidrag (Refereegranskat)
    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.

  • 17.
    Lundquist, Christian
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Hammarstrand, Lars
    Volvo Car Corporation, Sweden.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Road Intensity Based Mapping using Radar Measurements with a Probability Hypothesis Density Filter2011Rapport (Övrigt vetenskapligt)
    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.

  • 18.
    Lundquist, Christian
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Hammarstrand, Lars
    Volvo Car Corporation, Sweden.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Road Intensity Based Mapping using Radar Measurements with a Probability Hypothesis Density Filter2011Ingår i: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 59, nr 4, s. 1397-1408Artikel i tidskrift (Refereegranskat)
    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.

  • 19.
    Lundquist, Christian
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan. NIRA Dynamics AB.
    Özkan, Emre
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Tire Radii Estimation Using a Marginalized Particle Filter2014Ingår i: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 15, nr 2, s. 663-672Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this paper, the measurements of individual wheel speeds and the absolute position from a global positioning system are used for high-precision estimation of vehicle tire radii. The radii deviation from its nominal value is modeled as a Gaussian random variable and included as noise components in a simple vehicle motion model. The novelty lies in a Bayesian approach to estimate online both the state vector and the parameters representing the process noise statistics using a marginalized particle filter (MPF). Field tests show that the absolute radius can be estimated with submillimeter accuracy. The approach is tested in accordance with regulation 64 of the United Nations Economic Commission for Europe on a large data set (22 tests, using two vehicles and 12 different tire sets), where tire deflations are successfully detected, with high robustness, i.e., no false alarms. The proposed MPF approach outperforms common Kalman-filter-based methods used for joint state and parameter estimation when compared with respect to accuracy and robustness.

  • 20.
    Lundquist, Christian
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Orguner, Umut
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Estimating Polynomial Structures from Radar Data2010Rapport (Övrigt vetenskapligt)
    Abstract [en]

    Situation awareness for vehicular safety and autonomy functions includes knowledge of the drivable area. This area is normally constrained between stationary road-side objects as guard-rails, curbs, ditches and vegetation. We consider these as extended objects modeled by polynomials along the road, and propose an algorithm to track each polynomial based on noisy range and bearing detections, typically from a radar. A straightforward Kalman filter formulation of the problem suffers from the errors-in-variables (EIV) problem in that the noise enters the system model. We propose an EIV modification of the Kalman filter and demonstrates its usefulness using radar data from public roads.

  • 21.
    Lundquist, Christian
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Orguner, Umut
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Estimating Polynomial Structures from Radar Data2010Ingår i: Proceedings of the 13th International Conference on Information Fusion, Edinburgh, Scotland, 2010Konferensbidrag (Refereegranskat)
    Abstract [en]

    Situation awareness for vehicular safety and autonomy functions includes knowledge of the drivable area. This area is normally constrained between stationary road-side objects as guard-rails, curbs, ditches and vegetation. We consider these as extended objects modeled by polynomials along the road, and propose an algorithm to track each polynomial based on noisy range and bearing detections, typically from a radar. A straightforward Kalman filter formulation of the problem suffers from the errors-in-variables (EIV) problem in that the noise enters the system model. We propose an EIV modification of the Kalman filter and demonstrates its usefulness using radar data from public roads.

  • 22.
    Lundquist, Christian
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Orguner, Umut
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Extended Target Tracking Using Polynomials With Applications to Road-Map Estimation2010Rapport (Övrigt vetenskapligt)
    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.

  • 23.
    Lundquist, Christian
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Orguner, Umut
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Extended Target Tracking Using Polynomials With Applications to Road-Map Estimation2011Ingår i: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 59, nr 1, s. 15-26Artikel i tidskrift (Refereegranskat)
    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.

  • 24.
    Lundquist, Christian
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Orguner, Umut
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Schön, Thomas
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Tracking Stationary Extended Objects for Road Mapping using Radar Measurements2009Ingår i: Proceedings of the '09 IEEE Intelligent Vehicle Symposium, IEEE , 2009, s. 405-410Konferensbidrag (Refereegranskat)
    Abstract [en]

    It is getting more common that premium cars areequipped with a forward looking radar and a forward looking camera. The data is often used to estimate the road geometry, tracking leading vehicles, etc. However, there is valuable information present in the radar concerning stationary objects, that is typically not used. The present work shows how stationary objects, such as guard rails, can be modeled and tracked as extended objects using radar measurements. The problem is cast within a standard sensor fusion framework utilizing the Kalman filter. The approach has been evaluated on real datafrom highways and rural roads in Sweden.

  • 25.
    Lundquist, Christian
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Schön, Thomas
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Estimation of the Free Space in Front of a Moving Vehicle2009Ingår i: Proceedings of the '09 SAE World Congress & Exhibition, 2009Konferensbidrag (Refereegranskat)
    Abstract [en]

    There are more and more systems emerging making use of measurements from a forward looking radar and a forward looking camera. It is by now well known how to exploit this data in order to compute estimates of the road geometry, tracking leading vehicles, etc. However, there is valuable information present in the radar concerning stationary objects, that is typically not used. The present work shows how radar measurements of stationary objects can be used to obtain a reliable estimate of the free space in front of a moving vehicle. The approach has been evaluated on real data from highways and rural roads in Sweden.

  • 26.
    Lundquist, Christian
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Schön, Thomas
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Joint Ego-Motion and Road Geometry Estimation2011Ingår i: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 12, nr 4, s. 253-263Artikel i tidskrift (Refereegranskat)
    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.

  • 27.
    Lundquist, Christian
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Schön, Thomas
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Recursive Identification of Cornering Stiffness Parameters for an Enhanced Single Track Model2009Ingår i: Proceedings of the 15th IFAC Symposiumon System Identification, 2009, s. 1726-1731Konferensbidrag (Refereegranskat)
    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.

  • 28.
    Lundquist, Christian
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Schön, Thomas
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Road Geometry Estimation and Vehicle Tracking using a Single Track Model2008Ingår i: Proceedings of the 2008 IEEE Intelligent Vehicles Symposium, 2008, s. 144-149Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper is concerned with the, by now rather well studied, problem of integrated road geometry estimation and vehicle tracking. The main differences to the existing approaches are that we make use of an improved host vehicle model and a new dynamic model for the road. The problem is posed within a standard sensor fusion framework, allowing us to make good use of the available sensor information. The performance of the solution is evaluated using measurements from real and relevant traffic environments from public roads in Sweden. The experiments indicates that the gain in using the extended host vehicle model is most prominent when driving on country roads without any vehicles in front.

  • 29.
    Lundquist, Christian
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Schön, Thomas B.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Recursive Identification of Cornering Stiffness Parameters for an Enhanced Single Track Model2009Rapport (Övrigt vetenskapligt)
    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.

  • 30.
    Lundquist, Christian
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Schön, Thomas B.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Road Geometry Estimation and Vehicle Tracking using a Single Track Model2008Rapport (Övrigt vetenskapligt)
    Abstract [en]

    This paper is concerned with the, by now rather well studied, problem of integrated road geometry estimation and vehicle tracking. The main differences to the existing approaches are that we make use of an improved host vehicle model and a new dynamic model for the road. The problem is posed within a standard sensor fusion framework, allowing us to make good use of the available sensor information. The performance of the solution is evaluated using measurements from real and relevant traffic environments from public roads in Sweden. The experiments indicates that the gain in using the extended host vehicle model is most prominent when driving on country roads without any vehicles in front.

  • 31.
    Lundquist, Christian
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Schön, Thomas B.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Orguner, Umut
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Estimation of the Free Space in Front of a Moving Vehicle2009Rapport (Övrigt vetenskapligt)
    Abstract [en]

    There are more and more systems emerging making use of measurements from a forward looking radar and a forward looking camera. It is by now well known how to exploit this data in order to compute estimates of the road geometry, tracking leading vehicles, etc. However, there is valuable information present in the radar concerning stationary targets, that is typically not used. The present work shows how radar measurements of stationary targets can be used to provide a reliable estimate of the drivable space in front of a moving vehicle.

    In the present paper three conceptually different methods to estimate stationary objects or road borders are presented and compared. The first method considered is occupancy grid mapping, which discretizes the map surrounding the ego vehicle and the probability of occupancy is estimated for each grid cell. The second method applies a constrained quadratic program in order to estimate the road borders. The problem is stated as a constrained curve fitting problem. The third method associates the radar measurements to extended stationary objects and tracks them as extended targets.

    The required sensors, besides the standard proprioceptive sensors of a modern car, are a forward looking long range radar and a forward looking camera. Hence, there is no need to introduce any new sensors, it is just a matter of making better use of the sensor information that is already present in a modern car. The approach has been evaluated and tested on real data from highways and rural roads in Sweden and the results are very promising.

  • 32.
    Lundquist, Christian
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Schön, Thomas
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Situational Awareness and Road Prediction for Trajectory Control Applications2012Ingår i: Handbook of Intelligent Vehicles / [ed] Azim Eskandarian, Springer London, 2012, s. 365-396Kapitel i bok, del av antologi (Refereegranskat)
    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.

  • 33.
    Lundquist, Christian
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Skoglund, Martin
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Granström, Karl
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Glad, Torkel
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Insights from Implementing a System for Peer-Review2013Ingår i: IEEE Transactions on Education, ISSN 0018-9359, E-ISSN 1557-9638, Vol. 56, nr 3, s. 261-267Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Courses at the Master’s level in automatic control and signal processing cover mathematical theories and algorithms for control, estimation, and filtering. However, giving students practical experience in how to use these algorithms is also an important part of these courses. A goal is that the students should not only be able to understand and derive these algorithms, but also be able to apply them to real-life technical problems. The latter is achieved by assigning more time to the laboratory tutorials and designing them in such a way that the exercises are open for interpretation; an example of this would be giving the students more freedom to decide how to acquire the data needed to solve the given exercises.The students are asked to hand in a laboratory report in which they describe how they solved the exercises. This paper presents a double-blind peer-review process for laboratory reports, introduced at the Department of Electrical Engineering, Linköping University, Sweden. A survey was administered to students, and the results are summarized in this paper. Also discussed are the teachers’ experiences of peer review and of how students perform later in their education in writing their Master’s theses.

  • 34.
    Lundquist, Christian
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Özkan, Emre
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Tire Radii and Vehicle Trajectory Estimation Using a Marginalized Particle Filter2011Rapport (Övrigt vetenskapligt)
    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 axleis estimated with submillimeter accuracy.

  • 35.
    Lundquist, Christian
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Özkan, Emre
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Tire Radii and Vehicle Trajectory Estimation Using a Marginalized Particle FilterManuskript (preprint) (Övrigt vetenskapligt)
    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.

  • 36.
    Nilsson, Emil
    et al.
    Autoliv Electronics AB, Sweden.
    Lundquist, Christian
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Schön, Thomas
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Forslund, David
    Autoliv Electronics AB, Sweden.
    Roll, Jacob
    Autoliv Electronics AB, Sweden.
    Vehicle Motion Estimation Using an Infrared Camera2011Ingår i: Proceedings of the 18th IFAC World Congress / [ed] Bittanti, Sergio; Cenedese, Angelo; Zampieri, Sandro, Elsevier, 2011, s. 12952-12957Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper is concerned with vehicle motion estimation. The problem is formulated as a sensor fusion problem, where the vehicle motion is estimated based on the information from a far infrared camera, inertial sensors and the vehicle speed. This information is already present in premium cars. We are concerned with the off-line situation and the approach taken is to formulate the problem as a nonlinear least squares problem. In order to illustrate the performance of the proposed method experiments on rural roads in Sweden during night time driving have been performed. The results clearly indicates the efficacy of the approach.

  • 37.
    Orguner, Umut
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Lundquist, Christian
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Granström, Karl
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Extended Target Tracking with a Cardinalized Probability Hypothesis Density Filter2011Rapport (Övrigt vetenskapligt)
    Abstract [en]

    This paper presents a cardinalized probability hypothesis density (CPHD) filter for extended targets that can result in multiple measurements at each scan. The probability hypothesis density (PHD) filter for such targets has already been derived by Mahler and a Gaussian mixture implementation has been proposed recently. This work relaxes the Poisson assumptions of the extended target PHD filter in target and measurement numbers to achieve better estimation performance. A Gaussian mixture implementation is described. The early results using real data from a laser sensor confirm that the sensitivity of the number of targets in the extended target PHD filter can be avoided with the added flexibility of the extended target CPHD filter.

  • 38.
    Orguner, Umut
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Lundquist, Christian
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Granström, Karl
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Extended Target Tracking with a Cardinalized Probability Hypothesis Density Filter2011Ingår i: Proceedings of 2011 International Conference on Information Fusion (FUSION), 2011Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents a cardinalized probability hypothesis density (CPHD) filter for extended targets that can result in multiple measurements at each scan. The probability hypothesis density (PHD) filter for such targets has already been derived by Mahler and a Gaussian mixture implementation has been proposed recently. This work relaxes the Poisson assumptions of the extended target PHD filter in target and measurement numbers to achieve better estimation performance. A Gaussian mixture implementation is described. The early results using real data from a laser sensor confirm that the sensitivity of the number of targets in the extended target PHD filter can be avoided with the added flexibility of the extended target CPHD filter.

  • 39.
    Özkan, Emre
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Lundquist, Christian
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    A Bayesian Approach to Jointly Estimate Tire Radii and Vehicle Trajectory2011Ingår i: Proceedings of the International IEEE Conference on Intelligent Transportation Systems, Washington DC, USA: IEEE conference proceedings, 2011, s. 1-6Konferensbidrag (Refereegranskat)
    Abstract [en]

    High-precision estimation of vehicle tire radii is considered, based on measurements on individual wheel speeds and absolute position from a global navigation satellite system (GNSS). The wheel speed measurements are subject to noise with time-varying covariance that depends mainly on the road surface. The novelty lies in a Bayesian approach to estimate online the time-varying radii and noise parameters using a marginalized particle filter, where no model approximations are needed such as in previously proposed algorithms based on the extended Kalman filter. 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.

  • 40.
    Özkan, Emre
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Smidl, Vaclav
    Institute of Information Theory and Automation, Czech Republic.
    Saha, Saikat
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Lundquist, Christian
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Marginalized Adaptive Particle Filtering for Nonlinear Models with Unknown Time-Varying Noise Parameters2013Ingår i: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 49, nr 6, s. 1566-1575Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Knowledge of the noise distribution is typically crucial for the state estimation of general state-space models. However, properties of the noise process are often unknown in the majority of practical applications. The distribution of the noise may also be non-stationary or state dependent and that prevents the use of off-line tuning methods. For linear Gaussian models, Adaptive Kalman filters (AKF) estimate unknown parameters in the noise distributions jointly with the state. For nonlinear models, we provide a Bayesian solution for the estimation of the noise distributions in the exponential family, leading to a marginalized adaptive particle filter (MAPF) where the noise parameters are updated using finite dimensional sufficient statistics for each particle. The time evolution model for the noise parameters is defined implicitly as a Kullback-Leibler norm constraint on the time variability, leading to an exponential forgetting mechanism operating on the sufficient statistics. Many existing methods are based on the standard approach of augmenting the state with the unknown variables and attempting to solve the resulting filtering problem. The MAPF is significantly more computationally efficient than a comparable particle filter that runs on the full augmented state. Further, the MAPF can handle sensor and actuator offsets as unknown means in the noise distributions, avoiding the standard approach of augmenting the state with such offsets. We illustrate the MAPF on first a standard example, and then on a tire radius estimation problem on real data.

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