@article{M39F93C19,
title = "Inference on Driving Characteristic Based on Time-Series Partial Observation of Vehicle",
journal = "The Journal of Korean Institute of Communications and Information Sciences",
year = "2025",
issn = "1226-4717",
doi = "10.7840/kics.2025.50.6.858",
author = "Sangeun Park, Chanin Eom, Minhae Kwon",
keywords = "Autonomous driving system, Driving characteristic inference, Partial observation, Transformer, Long short-term memory, Multi-layer perceptron, Trajectory data",
abstract = "With the commercialization of autonomous driving systems, inferring the driving characteristics of adjacent vehicles has become increasingly important for effective interaction between autonomous and non-autonomous vehicles. Driving information can be collected using roadside units or vehicles to infer these characteristics.
This study proposes a model that infers driving characteristics based on trajectory data collected within a limited observation range. Specifically, we confirm the practicality of the proposed system by considering noise that may occur during the sensing process and using real driving datasets. Simulation results demonstrate that the proposed model outperforms baselines and proves highly practical, even in environments with limited observation."
}