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dc.contributor.advisorJonathan P. How.en_US
dc.contributor.authorJaipuria, Nikitaen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Mechanical Engineering.en_US
dc.date.accessioned2019-02-05T15:59:20Z
dc.date.available2019-02-05T15:59:20Z
dc.date.copyright2018en_US
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/120227
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 73-78).en_US
dc.description.abstractAutonomous driving on highways and freeways, as a feature, is already available in quite a few high-end commercial vehicles being sold today. Autonomous driving in urban environments, on the other hand, is still an active area of academic and industrial research [6], because of its relatively complex nature. Urban driving requires the self-driving vehicle to interact with not just other vehicles, but also other moving agents such as cyclists and pedestrians. Pedestrian trajectory prediction is challenging because of the relatively higher number of degrees of freedom in pedestrian movement and absence of uniform rules across different cities and different scenarios within a city. Furthermore, in scenarios such as intersections, context, such as pedestrian traffic lights, stop signs and sidewalk geometry, significantly influences pedestrian movement. The objective of this thesis is to present a general, context-aware, long term (order of few seconds) trajectory prediction model for pedestrians in urban intersections. To meet this objective, first, the Augmented Semi Nonnegative Sparse Coding (ASNSC) [13] framework, for trajectory prediction, is extended to embed context, and build the Context-aware Augmented Semi Nonnegative Sparse Coding (CASNSC) algorithm. For prediction in new, unseen intersections with different curbside geometries (orthogonal versus skewed), CASNSC is further extended to build the Transferable Augmented Semi Nonnegative Sparse Coding (TASNSC) algorithm. Urban intersections can at times vary significantly in the type of pedestrian behaviors encountered, even across intersections with similar geometries. For instance, faster, rule breaking students near a college campus versus slower pedestrians in a residential area. While TASNSC is capable of successfully transferring knowledge from one intersection to another, it lacks the ability to update its prediction model as, and when, new intersections are visited and novel behaviors are encountered. An online model, based on TASNSC, is also presented in this thesis to account for this particular limitation.en_US
dc.description.statementofresponsibilityby Nikita Jaipuria.en_US
dc.format.extent78 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectMechanical Engineering.en_US
dc.titleA general, context-aware pedestrian trajectory prediction modelen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineering
dc.identifier.oclc1083115314en_US


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