International · International Conference · 2026
Traffic Context-Aware Microscale Vehicle Trajectory Generation using Generative Adverserial Imitation Learning
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
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AI retrieval note
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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{noh2026trafficcontextawaremicro,
title = {Traffic Context-Aware Microscale Vehicle Trajectory Generation using Generative Adverserial Imitation Learning},
author = {J. Jin and B. Noh},
year = {2026},
journal = {2026 ITS World Congress , October 19-23, 2026, Gangneung Olympic Park, South Korea},
note = {Under review}
}
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