Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Probabilistic Model for Interaction Aware Planning in Merge Scenarios
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. (CVAP)
Linköping University .
Linköping Universtiy.
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. (CAS/CVAP/CSC)
2017 (English)In: IEEE Transactions on Intelligent Vehicles, ISSN 2379-8858, Vol. 2, no 2, p. 133-146Article in journal (Refereed) Published
Abstract [en]

Merge scenarios confront drivers with some of the most complicated driving maneuvers in every day driving, requiring anticipatory reasoning of positions of other vehicles, and the own vehicles future trajectory. In congested traffic it might be impossible to merge without cooperation of up-stream vehicles, therefore, it is essential to gauge the effect of our own trajectory when planning a merge maneuver. For an autonomous vehicle to perform a merge maneuver in congested traffic similar capabilities are required. This includes a model describing the future evolution of the scene that allows for optimizing the autonomous vehicle's planned trajectory with respect to risk, comfort, and dynamical limitations. We present a probabilistic model that explicitly models interaction between vehicles and allows for evaluating the utility of a large number of candidate trajectories of an autonomous vehicle using a receding horizon approach in order to select an appropriate merge maneuver. The model is an extension of the intelligent driver model and the modeled behavior of other vehicles are adjusted using on-line model parameter estimation in order to give better predictions. The prediction model is evaluated using naturalistic traffic data and the merge maneuver planner is evaluated in simulation.

Place, publisher, year, edition, pages
IEEE, 2017. Vol. 2, no 2, p. 133-146
Keywords [en]
Trajectory, Predictive models, Planning, Vehicles, Acceleration, Probabilistic logic, Data models
National Category
Robotics
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-220357DOI: 10.1109/TIV.2017.2730588OAI: oai:DiVA.org:kth-220357DiVA, id: diva2:1167577
Projects
VINNOVA IQMatic
Funder
VINNOVA, 2012-04626Available from: 2017-12-19 Created: 2017-12-19 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
Keywords
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

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full texthttp://ieeexplore.ieee.org/abstract/document/7987753/

Search in DiVA

By author/editor
Ward, ErikFolkesson, John
By organisation
Robotics, perception and learning, RPLCentre for Autonomous Systems, CAS
Robotics

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 143 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf