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Jonathan P. How. Ferguson, Sarah Kathryn Massachusetts Institute of Technology. Department of Aeronautics and Astronautics. Massachusetts Institute of Technology. Department of Aeronautics and Astronautics 2014-10-08T15:28:29Z 2014-10-08T15:28:29Z 2014 2014 http://hdl.handle.net/1721.1/90777 891566789 Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2014. CD-ROM contains film. Cataloged from PDF version of thesis. Includes bibliographical references (pages 97-102). To plan safe trajectories in urban environments, autonomous vehicles must be able to interact safely and intelligently with other dynamic agents. Due to the inherent structure of these environments, drivers and pedestrians tend to exhibit a common set of motion patterns. The challenges are therefore to learn these motion patterns such that they can be used to predict future trajectories, and to plan safe paths that incorporate these predictions. This thesis considers the modeling and robust avoidance of pedestrians in real time. Pedestrians are particularly difficult to model, as their motion patterns are often uncertain and/or unknown a priori. The modeling approach incorporates uncertainty in both intent (i.e., where is the pedestrian going?) and trajectory associated with each intent (i.e., how will he/she get to this location?), both of which are necessary for robust collision avoidance. A novel changepoint detection and clustering algorithm (Changepoint-DPGP) is presented to enable quick detection of changes in pedestrian behavior and online learning of new behaviors not previously observed in prior training data. The resulting long-term movement predictions demonstrate improved accuracy in terms of both intent and trajectory prediction, relative to existing methods which consider only intent or trajectory. An additional contribution of this thesis is the integration of these predictions with a chance-constrained motion planner, such that trajectories which are probabilistically safe to pedestrian motions can be identified in real-time. Hardware components and relevant control and data acquisition algorithms for an autonomous test vehicle are implemented and developed. Experiments demonstrate that an autonomous mobile robot utilizing this framework can accurately predict pedestrian motion patterns from onboard sensor/perception data and safely navigate within a dynamic environment by Sarah Kathryn Ferguson. S.M. 102 pages eng Massachusetts Institute of Technology System requirements: Windows and CD-ROM drive. M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Aeronautics and Astronautics. Real-time predictive modeling and robust avoidance of pedestrians with uncertain, changing intentions Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="63f69bfd-fef2-4792-9f66-3e050c524820"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Real-time predictive modeling and robust avoidance of pedestrians with uncertain, changing intentions</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2014</PublicationDate> <Authors> <Author> <DisplayName>Ferguson, Sarah Kathryn</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Aeronautics and Astronautics.</Keyword> <Abstract>To plan safe trajectories in urban environments, autonomous vehicles must be able to interact safely and intelligently with other dynamic agents. Due to the inherent structure of these environments, drivers and pedestrians tend to exhibit a common set of motion patterns. The challenges are therefore to learn these motion patterns such that they can be used to predict future trajectories, and to plan safe paths that incorporate these predictions. This thesis considers the modeling and robust avoidance of pedestrians in real time. Pedestrians are particularly difficult to model, as their motion patterns are often uncertain and/or unknown a priori. The modeling approach incorporates uncertainty in both intent (i.e., where is the pedestrian going?) and trajectory associated with each intent (i.e., how will he/she get to this location?), both of which are necessary for robust collision avoidance. A novel changepoint detection and clustering algorithm (Changepoint-DPGP) is presented to enable quick detection of changes in pedestrian behavior and online learning of new behaviors not previously observed in prior training data. The resulting long-term movement predictions demonstrate improved accuracy in terms of both intent and trajectory prediction, relative to existing methods which consider only intent or trajectory. An additional contribution of this thesis is the integration of these predictions with a chance-constrained motion planner, such that trajectories which are probabilistically safe to pedestrian motions can be identified in real-time. Hardware components and relevant control and data acquisition algorithms for an autonomous test vehicle are implemented and developed. Experiments demonstrate that an autonomous mobile robot utilizing this framework can accurately predict pedestrian motion patterns from onboard sensor/perception data and safely navigate within a dynamic environment</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>