Year: 2025
Type: conference-paper
Abstract: Roundabouts present unique challenges in intelligent transportation systems due to their complex geometry, dynamic interactions, and the limited availability of high-quality datasets. Existing methodologies typically lack contextual richness and perform inadequately when addressing the non-linear nature of roundabout behavior. This paper introduces a data augmentation framework that leverages large language models (LLMs), specifically a fine-tuned GPT-2, to enrich roundabout datasets with semantically meaningful behavioral patterns. Our approach initiates with extracting vehicle features from real-world video using YOLOv8 and DeepSORT, subsequently using a feedforward neural network (FNN) to extract three latent feature, and training GPT-2 on this corpus to generate high-level behavioral labels, thereby semantically enriching the dataset's diversity and semantic depth. To validate the effectiveness of our framework, we train a long short-term memory (LSTM) model for trajectory prediction. Evaluation with an LSTM predictor shows that models trained on synthetic GPT-generated data outperform those using original data, with lower average relative error (1.46% vs. 1.72%) and improved accuracy at the 100th percentile (62% vs. 54%). Experimental results show that our augmented synthetic dataset is capable of improving the robustness of prediction, establishing a new paradigm for intelligent traffic modeling through the integration of foundational models. Our code is available at https://github.com/Rebecca689/llm-roundabout.
Authors: Xiaotong Zhou, Zhenhui Yuan, Yi Han, Tianhua Xu, Jaiwei Wang
Institutions: University of Warwick, Wuhan University of Technology
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Awards: G3633283556, G8193076724
OpenAlex ID: W4411996836