Trajectory Prediction Learning using Deep Generative Models

dc.contributor.advisorPapagelis, Manos
dc.contributor.authorLi, Jing
dc.date.accessioned2024-03-18T18:12:42Z
dc.date.available2024-03-18T18:12:42Z
dc.date.issued2024-03-16
dc.date.updated2024-03-16T10:49:19Z
dc.degree.disciplineComputer Science
dc.degree.levelMaster's
dc.degree.nameMSc - Master of Science
dc.description.abstractTrajectory prediction involves estimating an object's future path using its current state and historical data, with applications in autonomous vehicles, robotics, and human motion analysis. Deep learning methods trained on historical data have been applied to this task, but they struggle with complex spatial dependencies due to the intricate nature of trajectory data and dynamic environments. We introduce TrajLearn, a novel trajectory prediction model using generative models and higher-order mobility flow representations (hexagons). TrajLearn, given a trajectory's recent history and current state, predicts its next k steps. It employs a variant of beam search for exploring multiple paths, ensuring spatial continuity. Our experiments demonstrate that TrajLearn surpasses current leading methods and other baselines by about 60% on various real-world datasets. We also explore different prediction horizons (k values), perform resolution sensitivity analysis, and conduct an ablation study to evaluate the contributions of different model components.
dc.identifier.urihttps://hdl.handle.net/10315/41931
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectComputer science
dc.subject.keywordsHuman mobility
dc.subject.keywordsTrajectory prediction
dc.subject.keywordsDeep generative models
dc.titleTrajectory Prediction Learning using Deep Generative Models
dc.typeElectronic Thesis or Dissertation

Files

Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Li_Jing_2024_Masters.pdf
Size:
6.48 MB
Format:
Adobe Portable Document Format
License bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
license.txt
Size:
1.87 KB
Format:
Plain Text
Description:
No Thumbnail Available
Name:
YorkU_ETDlicense.txt
Size:
3.39 KB
Format:
Plain Text
Description:

Collections