A Data-driven Model for Lane-changing in Traffic Simulation

dc.contributor.authorBi, Huikunen_US
dc.contributor.authorMao, Tianluen_US
dc.contributor.authorWang, Zhaoqien_US
dc.contributor.authorDeng, Zhigangen_US
dc.contributor.editorLadislav Kavan and Chris Wojtanen_US
dc.date.accessioned2016-07-10T12:52:09Z
dc.date.available2016-07-10T12:52:09Z
dc.date.issued2016
dc.description.abstractIn this paper, we propose a new data-driven model to simulate the process of lane-changing in traffic simulation. Specifically, we first extract the features from surrounding vehicles that are relevant to the lane-changing of the subject vehicle. Then, we learn the lane-changing characteristics from the ground-truth vehicle trajectory data using randomized forest and back-propagation neural network algorithms. Our method can make the subject vehicle to take account of more gap options on the target lane to cut in as well as achieve more realistic lane-changing trajectories for the subject vehicle and the follower vehicle. Through many experiments and comparisons with selected state-of-the-art methods, we demonstrate that our approach can soundly outperform them in terms of the accuracy and quality of lane-changing simulation. Our model can be flexibly used together with a variety of existing car-following models to produce natural traffic animations in various virtual environments.en_US
dc.description.sectionheadersGroup Dynamics
dc.description.seriesinformationEurographics/ ACM SIGGRAPH Symposium on Computer Animation
dc.identifier.doi10.2312/sca.20161232
dc.identifier.isbn978-3-03868-009-3
dc.identifier.issn1727-5288
dc.identifier.pages149-158
dc.identifier.urihttps://doi.org/10.2312/sca.20161232
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/sca20161232
dc.publisherThe Eurographics Associationen_US
dc.subjectI.3.7 [Computer Graphics]
dc.subjectThree Dimensional Graphics and Realism
dc.subjectAnimation
dc.titleA Data-driven Model for Lane-changing in Traffic Simulationen_US
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