使用时间图神经网络的交互感知个性化车辆轨迹预测
Interaction-Aware Personalized Vehicle Trajectory Prediction Using Temporal Graph Neural Networks
AI总结：
针对通用车辆轨迹预测忽视个体驾驶风格的问题，提出融合GCN与LSTM并经大规模预训练和驾驶员微调的交互感知个性化方法，提升了长时域预测精度。
摘要：
准确预测车辆轨迹对于高级驾驶辅助系统和自动驾驶车辆至关重要。现有方法主要依赖从大型数据集中获得的通用轨迹预测，忽视了个体驾驶员的个性化驾驶模式。为弥补这一差距，我们提出一种结合时间图神经网络的、具有交互感知能力的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）来建模目标车辆与其周围交通之间的时空交互。为实现预测个性化，我们建立了利用迁移学习的流程：模型首先在大规模轨迹数据集上进行预训练，随后使用每位驾驶员特有的驾驶数据对其进行微调。我们采用人在回路仿真来收集个性化的自然驾驶轨迹以及相应的周围车辆轨迹。实验结果表明，与通用模型相比，我们的个性化GCN-LSTM模型性能更优，尤其是在较长预测时域上。此外，该个性化模型优于未经预训练而单独建立的模型，凸显了在大型数据集上进行预训练以避免过拟合的重要意义。通过引入个性化，本方法提高了轨迹预测的准确性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
