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Calculation of collision probability for autonomous vehicles using trajectory prediction

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TitleInfo
Title
Calculation of collision probability for autonomous vehicles using trajectory prediction
Name (type = personal)
NamePart (type = family)
Powar
NamePart (type = given)
Gayatri
NamePart (type = date)
1988-
DisplayForm
Gayatri Powar
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author
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Gajic
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Zoran
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Zoran Gajic
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Advisory Committee
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chair
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Lindqvist
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Janne
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Janne Lindqvist
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Advisory Committee
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internal member
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Baruh
NamePart (type = given)
Haim
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Haim Baruh
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = corporate)
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Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Graduate School - New Brunswick
Role
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school
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Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2016
DateOther (qualifier = exact); (type = degree)
2016-10
CopyrightDate (encoding = w3cdtf); (qualifier = exact)
2016
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
The aim of this thesis is to create a decision making algorithm. The goal would be to check the feasibility of the current maneuver by nding the probability of collision between the subject and target vehicles. These outputs can be used by other Advanced Driver Assistance System (ADAS) features including path planner, lateral control, longitudinal control, etc. We make use of some sensors like camera/radar (simulated data) and fuse these together for better estimation of measurements. With earlier experience with cameras, they are really poor at giving longitudinal distances whereas radars give more accurate measurements longitudinally. Using these measurements about targets ahead in the environment, we predict the trajectories of the obstacles/targets as well as the subject vehicle (autonomous vehicle). The algorithm predicts if it is safe to continue with the current maneuver in the near future for several seconds ahead of time, and makes the decision if the maneuver is possible. The results are obtained using probabilistic approach whether the future trajectories are going to collide. The thesis primarily focuses on target tracking, e cient sensor data fusion and future collision estimation. With the lessons learnt using existing literature an e cient approach is employed. The simulation is performed in PreScan simulator and MATLAB. Enhancements in the sensor data fusion using standby measurements and quasi-decentralized approach to combine measurements to yield improved results and achieve better scalability are proposed and implemented.
Subject (authority = RUETD)
Topic
Electrical and Computer Engineering
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_7451
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xi, 63 p. : ill.)
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references
Subject (authority = ETD-LCSH)
Topic
Autonomous vehicles
Note (type = statement of responsibility)
by Gayatri Powar
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/T3F19215
Genre (authority = ExL-Esploro)
ETD graduate
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Rights

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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Powar
GivenName
Gayatri
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2016-07-26 12:18:46
AssociatedEntity
Name
Gayatri Powar
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

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2016-07-28T13:57:46
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2016-07-28T13:57:46
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