Show simple item record

Data creatorMüller, Johannesdc.creator
Data creatorStrohbeck, Jandc.creator
Data creatorHerrmann, Martindc.creator
Data creatorBuchholz, Michaeldc.creator
Date of accession2020-11-19T14:02:18Zdc.date.accessioned
Available in OPARU since2020-11-19T14:02:18Zdc.date.available
Year of creation2020dc.date.created
Date of first publication2020-11-19dc.date.issued
AbstractMotion planning at urban intersections that accounts for the situation context, handles occlusions and deals with measurement and prediction uncertainty is a major challenge on the way to urban automated driving. As classical methods subdivide the motion planning into decision making and trajectory planning, they either come with a narrowed solution set or finding a feasible trajectory is not guaranteed. In this work, motion planning is formulated as an optimal control problem (OCP) and holistically solved for exploring all available decision options. The OCP is parametrized and simplified according to the situation context extracted from map and perception information. Occlusions are resolved using the external perception of infrastructure-mounted sensors. Yet, instead of merging external and ego perception with track-to-track (T2T) fusion, the information is used in parallel. The uncertainties are handled by a risk model that bridges the gap between set-based methods and probabilistic approaches. Particularly, for vanishing risk, the formal guarantees of set-based methods are inherited, while otherwise, the guarantees are softened to guarantees in a probabilistic sense. This video shows a short presentation of the motion planning approach as well as results from the real-world experiments.dc.description.abstract
Languageendc.language.iso
PublisherUniversität Ulmdc.publisher
LicenseStandarddc.rights
Link to license texthttps://oparu.uni-ulm.de/xmlui/license_v3dc.rights.uri
KeywordMotion planningdc.subject
Keywordconnected automated drivingdc.subject
Keywordintersection scenariodc.subject
Dewey Decimal GroupDDC 600 / Technology (Applied sciences)dc.subject.ddc
LCSHAutomobile driving--Automationdc.subject.lcsh
LCSHDecision makingdc.subject.lcsh
LCSHMotion perception (Vision)dc.subject.lcsh
TitleVideo: Motion Planning for Connected Automated Vehicles at Occluded Intersections With Infrastructure Sensorsdc.title
Resource typeBewegte Bilderdc.type
DOIhttp://dx.doi.org/10.18725/OPARU-33797dc.identifier.doi
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-oparu-33859-9dc.identifier.urn
GNDKreuzungdc.subject.gnd
FacultyFakultät für Ingenieurwissenschaften, Informatik und Psychologieuulm.affiliationGeneral
InstitutionInstitut für Mess-, Regel- und Mikrotechnikuulm.affiliationSpecific
DCMI TypeImageuulm.typeDCMI
CategoryForschungsdatenuulm.category
EU project uulmICT4CART / ICT Infrastructure for Connected and Automated Road Transport / EC / H2020 / 768953uulm.projectEU
Bibliographyuulmuulm.bibliographie


Files in this item

This item appears in the following Collection(s)

Show simple item record