Large Language Model Based Roundabout Dataset Augmentation for Trajectory Prediction

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.