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Joint Ego-Motion and Road Geometry Estimation
Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan. (Sensor Fusion)
Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan. (Sensor Fusion)
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. Vol. 12, nr 4, s. 253-263
Emneord [en]
Sensor fusion, Single track model, Bicycle model, Road geometry estimation, Extended Kalman filter
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-51243DOI: 10.1016/j.inffus.2010.06.007ISI: 000293207500004OAI: oai:DiVA.org:liu-51243DiVA, id: diva2:273742
Prosjekter
IVSS - SEFSTilgjengelig fra: 2011-01-13 Laget: 2009-10-23 Sist oppdatert: 2017-12-12bibliografisk kontrollert
Inngår i avhandling
1. Automotive Sensor Fusion for Situation Awareness
Åpne denne publikasjonen i ny fane eller vindu >>Automotive Sensor Fusion for Situation Awareness
2009 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Linköping: Linköping University Electronic Press, 2009. s. 76
Serie
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1422
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-51226 (URN)LiU-TEK-LIC-2009:30 (Lokal ID)978-91-7393-492-3 (ISBN)LiU-TEK-LIC-2009:30 (Arkivnummer)LiU-TEK-LIC-2009:30 (OAI)
Presentation
2009-11-20, Visionen, B-building, Campus Valla, Linköpings universitet, Linköping, 10:15 (engelsk)
Opponent
Veileder
Prosjekter
IVSS - SEFS
Tilgjengelig fra: 2009-10-23 Laget: 2009-10-22 Sist oppdatert: 2020-03-10bibliografisk kontrollert
2. Sensor Fusion for Automotive Applications
Åpne denne publikasjonen i ny fane eller vindu >>Sensor Fusion for Automotive Applications
2011 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

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

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

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

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

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

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

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

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