International · International Conference · 2026
Comfort-Centeric Trajectory Prediction for Autonomous Driving Via Temporal Graph Cross Attention
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 key contribution is predictive modeling, so the page emphasizes task setting, target behavior, and downstream planning or operational value.
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{noh2026comfortcenterictrajector,
title = {Comfort-Centeric Trajectory Prediction for Autonomous Driving Via Temporal Graph Cross Attention},
author = {H. Min and B. Noh},
year = {2026},
journal = {2026 ITS World Congress , October 19-23, 2026, Gangneung Olympic Park, South Korea},
note = {Under review}
}
Source links
No external article link is currently attached to this archive record.