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Towards Risk Minimizing Trajectory Planning in On-Road Scenarios
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.ORCID iD: 0000-0002-7796-1438
2018 (English)In: IEEE Intelligent Vehicles Symposium, Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2018, p. 490-497Conference paper, Published paper (Refereed)
Abstract [en]

Trajectory planning for autonomous vehicles should attempt to minimize expected risk given noisy sensor data and uncertain predictions of the near future. In this paper, we present a trajectory planning approach for on-road scenarios where we use a graph search approximation. Uncertain predictions of other vehicles are accounted for by a novel inference technique that allows efficient calculation of the probability of dangerous outcomes for set of modeled situation types.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2018. p. 490-497
Keywords [en]
Intelligent vehicle highway systems, Risk perception, Roads and streets, Trajectories, Vehicles, Autonomous Vehicles, Expected risk, Graph search, Inference techniques, Noisy sensors, Trajectory Planning, Highway planning
National Category
Civil Engineering
Identifiers
URN: urn:nbn:se:kth:diva-247125DOI: 10.1109/IVS.2018.8500643Scopus ID: 2-s2.0-85056766110ISBN: 9781538644522 (print)OAI: oai:DiVA.org:kth-247125DiVA, id: diva2:1301885
Conference
2018 IEEE Intelligent Vehicles Symposium, IV 2018, 26 September 2018 through 30 September 2018
Note

QC 20190403

Available from: 2019-04-03 Created: 2019-04-03 Last updated: 2019-04-03Bibliographically approved

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Folkesson, John

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