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Vehicle localization with low cost radar sensors
KTH, School of Computer Science and Communication (CSC). (CVAP)
KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. (CAS/RPL/CSC)ORCID iD: 0000-0002-7796-1438
2016 (English)In: Intelligent Vehicles Symposium (IV), 2016 IEEE, Institute of Electrical and Electronics Engineers (IEEE), 2016Conference paper, Published paper (Refereed)
Abstract [en]

Autonomous vehicles rely on GPS aided by motion sensors to localize globally within the road network. However, not all driving surfaces have satellite visibility. Therefore, it is important to augment these systems with localization based on environmental sensing such as cameras, lidar and radar in order to increase reliability and robustness. In this work we look at using radar for localization. Radar sensors are available in compact format devices well suited to automotive applications. Past work on localization using radar in automotive applications has been based on careful sensor modeling and Sequential Monte Carlo, (Particle) filtering. In this work we investigate the use of the Iterative Closest Point, ICP, algorithm together with an Extended Kalman filter, EKF, for localizing a vehicle equipped with automotive grade radars. Experiments using data acquired on public roads shows that this computationally simpler approach yields sufficiently accurate results on par with more complex methods.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016.
Keyword [en]
Sensors, Iterative closest point algorithm, Roads, Vehicles, Spaceborne radar, Laser radar
National Category
Robotics
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-192860DOI: 10.1109/IVS.2016.7535489Scopus ID: 2-s2.0-84983292972ISBN: 978-1-5090-1821-5 (print)OAI: oai:DiVA.org:kth-192860DiVA, id: diva2:972553
Conference
Intelligent Vehicles Symposium (IV), 2016 IEEE
Projects
iQMatic VINNOVA
Funder
VINNOVA, 2013-03964
Note

QC 20160929

Available from: 2016-09-21 Created: 2016-09-21 Last updated: 2018-03-27Bibliographically approved
In thesis
1. Models Supporting Trajectory Planning in Autonomous Vehicles
Open this publication in new window or tab >>Models Supporting Trajectory Planning in Autonomous Vehicles
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Autonomous vehicles have the potential to drastically improve the safety, efficiency and cost of transportation. Instead of a driver, an autonomous vehicle is controlled by an algorithm, offering improved consistency and the potential to eliminate human error from driving: by far the most common cause of accidents.

Data collected from different types of sensors, along with prior information such as maps, are used to build models of the surrounding traffic scene, encoding relevant aspects of the driving problem.These models allow the autonomous vehicle to plan how it will drive, optimizing comfort, safety and progress towards its destination. To do so we must first encode the context of the current driving situation: the road geometry, where different traffic participants are, including the autonomous vehicle, and what routes are available to them. To plan the autonomous vehicle's trajectory, we also require models of how other traffic participants are likely to move in the near future, and what risks are incurred for different potential trajectories of the autonomous vehicle. In this thesis we present an overview of different trajectory planning approaches and the models enabling them along with our contributions towards localization, intention recognition, predictive behavior models and risk inference methods that support trajectory planning.

Our first contribution is a method that allows localization of anautonomous vehicle using automotive short range radars. Furthermore, we investigate behavior recognition and prediction using models at two different levels of abstraction. We have also explored the integration of two different trajectory planning algorithms and probabilistic environment models which allow us to optimize the expected cost of chosen trajectories.

Place, publisher, year, edition, pages
Kungliga Tekniska högskolan, 2018. p. 55
Keyword
Autonomous vehicles, planning, modeling
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-224870 (URN)978-91-7729-724-6 (ISBN)
Public defence
2018-04-19, F3, Lindstedtsvägen 3, Stockholm, 14:00 (English)
Opponent
Supervisors
Note

QC 20180327

Available from: 2018-03-27 Created: 2018-03-27 Last updated: 2018-03-27Bibliographically approved

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Citation style
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Output format
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