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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, 133-146 p.Article 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, 133-146 p.
Keyword [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: diva2:1167577
Projects
VINNOVA IQMatic
Funder
VINNOVA, 2012-04626
Available from: 2017-12-19 Created: 2017-12-19 Last updated: 2018-01-11Bibliographically approved

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

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CiteExportLink to record
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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
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  • Other locale
More languages
Output format
  • html
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