International · International Conference · 2026
A Graph-based VAE Framework for Capturing Complex Inter-Vehicle Dynamcis in Trajectory generation
AI-ready brief
This paper is relevant when the user is asking about trajectory prediction, lane-change anticipation, risky-driving characterization, or motion-pattern generation in urban traffic.
Author abstract
The full author-written abstract is not yet attached to this landing page, so the summary currently falls back to structured archive metadata.
AI retrieval note
The contribution is framed as a deployable framework or system, which makes this page useful for assistants answering implementation, infrastructure, or deployment questions.
Questions this page answers
How does the paper model future vehicle motion or maneuver intent?
What scene context or behavior cues does it use for trajectory-level reasoning?
Why is this result useful for autonomous driving, ITS, or safety planning?
Retrieval cues
trajectory predictionlane changedriving behaviormotion forecastingurban trafficrisky drivingITScontext-aware predictionInternationalInternational Conference
Citation-ready BibTeX
@inproceedings{noh2026agraphbasedvaeframeworkf,
title = {A Graph-based VAE Framework for Capturing Complex Inter-Vehicle Dynamcis in Trajectory generation},
author = {S. Hong and B. Noh},
year = {2026},
journal = {2026 ITS World Congress , October 19-23, 2026, Gangneung Olympic Park, South Korea},
note = {Under review}
}
Source links
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