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Automotive Sensor Fusion for Situation Awareness
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. (Sensor Fusion)
2009 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

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

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

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

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

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press , 2009. , 76 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1422
National Category
Information Science
Identifiers
URN: urn:nbn:se:liu:diva-51226Local ID: LiU-TEK-LIC-2009:30ISBN: 978-91-7393-492-3 (print)OAI: oai:DiVA.org:liu-51226DiVA: diva2:273561
Presentation
2009-11-20, Visionen, B-building, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Opponent
Supervisors
Projects
IVSS - SEFS
Available from: 2009-10-23 Created: 2009-10-22 Last updated: 2009-10-23Bibliographically approved
List of papers
1. Joint Ego-Motion and Road Geometry Estimation
Open this publication in new window or tab >>Joint Ego-Motion and Road Geometry Estimation
2011 (English)In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 12, no 4, 253-263 p.Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
Elsevier, 2011
Keyword
Sensor fusion, Single track model, Bicycle model, Road geometry estimation, Extended Kalman filter
National Category
Signal Processing Control Engineering
Identifiers
urn:nbn:se:liu:diva-51243 (URN)10.1016/j.inffus.2010.06.007 (DOI)000293207500004 ()
Projects
IVSS - SEFS
Available from: 2011-01-13 Created: 2009-10-23 Last updated: 2017-12-12Bibliographically approved
2. Recursive Identification of Cornering Stiffness Parameters for an Enhanced Single Track Model
Open this publication in new window or tab >>Recursive Identification of Cornering Stiffness Parameters for an Enhanced Single Track Model
2009 (English)In: Proceedings of the 15th IFAC Symposiumon System Identification, 2009, 1726-1731 p.Conference paper, Published paper (Refereed)
Abstract [en]

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

Series
LiTH-ISY-R, ISSN 1400-3902 ; 2893
Keyword
Recursive estimation, Recursive least square, Vehicle dynamics, Gray box model, Tire-road interaction
National Category
Engineering and Technology Control Engineering
Identifiers
urn:nbn:se:liu:diva-45372 (URN)10.3182/20090706-3-FR-2004.00287 (DOI)82231 (Local ID)978-3-902661-47-0 (ISBN)82231 (Archive number)82231 (OAI)
Conference
15th IFAC Symposium on System Identification, Saint-Malo, France, July, 2009
Available from: 2011-12-16 Created: 2009-10-10 Last updated: 2013-02-20Bibliographically approved
3. Estimation of the Free Space in Front of a Moving Vehicle
Open this publication in new window or tab >>Estimation of the Free Space in Front of a Moving Vehicle
2009 (English)In: Proceedings of the '09 SAE World Congress & Exhibition, 2009Conference paper, Published paper (Refereed)
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.

Keyword
Road geometry, Weighted least squares, Quadratic program, Road borders, Free space estimation, Automotive radar, Road mapping
National Category
Engineering and Technology Control Engineering
Identifiers
urn:nbn:se:liu:diva-44998 (URN)10.4271/2009-01-1288 (DOI)79290 (Local ID)978-0-7680-2126-4 (ISBN)79290 (Archive number)79290 (OAI)
Conference
SAE World Congress & Exhibition, April 2009, Detroit, MI, USA
Available from: 2011-12-16 Created: 2009-10-10 Last updated: 2013-02-21Bibliographically approved
4. Tracking Stationary Extended Objects for Road Mapping using Radar Measurements
Open this publication in new window or tab >>Tracking Stationary Extended Objects for Road Mapping using Radar Measurements
2009 (English)In: Proceedings of the '09 IEEE Intelligent Vehicle Symposium, IEEE , 2009, 405-410 p.Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
IEEE, 2009
Series
IEEE Intelligent Vehicles Symposium. Proceedings, ISSN 1931-0587
Keyword
Extended objects, Object detection, Radar imaging, Road vehicle radar, Object tracking, Road mapping, Stationary objects
National Category
Engineering and Technology Control Engineering
Identifiers
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)
Conference
'09 IEEE Intelligent Vehicle Symposium, Xi’an, China, June, 2009
Projects
IVSS - SEFSMOVIII
Available from: 2011-08-12 Created: 2009-05-09 Last updated: 2013-07-22Bibliographically approved

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