Accurate prediction of vehicle trajectories is vital for advanced driver assistance systems and autonomous vehicles. Existing methods mainly rely on generic trajectory predictions derived from large datasets, overlooking the personalized driving patterns of individual drivers. To address this gap, we propose an approach for interaction-aware personalized vehicle trajectory prediction that incorporates temporal graph neural networks. Our method utilizes Graph Convolution Networks (GCN) and Long Short-Term Memory (LSTM) to model the spatio-temporal interactions between target vehicles and their surrounding traffic. To personalize the predictions, we establish a pipeline that leverages transfer learning: the model is initially pre-trained on a large-scale trajectory dataset and then fine-tuned for each driver using their specific driving data. We employ human-in-the-loop simulation to collect personalized naturalistic driving trajectories and corresponding surrounding vehicle trajectories. Experimental results demonstrate the superior performance of our personalized GCN-LSTM model, particularly for longer prediction horizons, compared to its generic counterpart. Moreover, the personalized model outperforms individual models created without pre-training, emphasizing the significance of pre-training on a large dataset to avoid overfitting. By incorporating personalization, our approach enhances trajectory prediction accuracy.

翻译：车辆轨迹的精确预测对于高级驾驶辅助系统和自动驾驶汽车至关重要。现有方法主要依赖从大规模数据集推导出的通用轨迹预测，忽视了个体驾驶员的个性化驾驶模式。为弥补这一不足，我们提出了一种融合时序图神经网络的交互感知个性化车辆轨迹预测方法。该方法利用图卷积网络（GCN）和长短期记忆网络（LSTM）建模目标车辆与周边交通的时空交互。为实现个性化预测，我们构建了基于迁移学习的框架：模型首先在大规模轨迹数据集上预训练，随后针对每位驾驶员的特定驾驶数据进行微调。我们采用人在回路仿真技术采集个性化自然驾驶轨迹及对应的周边车辆轨迹。实验结果表明，与通用模型相比，我们的个性化GCN-LSTM模型在较长预测时域下展现出更优性能。此外，个性化模型优于未经预训练单独训练的模型，突显了在大规模数据集上进行预训练以避免过拟合的重要性。通过引入个性化机制，该方法有效提升了轨迹预测精度。