International · International Conference · 2025
Traffic Context-Augmented Vehicle Trajectory Prediction Framework using Multimodal LLM
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{noh2025trafficcontextaugmentedv,
title = {Traffic Context-Augmented Vehicle Trajectory Prediction Framework using Multimodal LLM},
author = {J. Im and B. Kim and J. Jin and B. Noh},
year = {2025},
journal = {2025 IEEE Intelligent Transportation System Conference (ITSC) , Nov 21-28, 2025, Gold Coast, Australia}
}
Source links
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