Effectively predicting interactive behaviors of traffic participants in the urban road is the key to successful decision-making and motion planning of intelligent vehicles. In this article, based on the data collected from vehicle on-board sensors, a graph-neural-network-based multitask learning framework (GNN-MTLF) is proposed to accurately predict trajectories of traffic participants with interactive behaviors. The interactive behavior considered in this research includes interactive events and trajectories that are modeled as spatial-temporal graphs using the GNN. Under the GNN-MTLF, the prediction process contains two main parts: recognition of interactive events and prediction of interactive trajectories. An integrated loss function is designed for multitask learning with the purpose of prediction and recognition. The proposed framework is verified using naturalistic driving data in the urban road. Experimental results show a superior performance of the GNN-MTLF compared to baseline methods and the potential for improving the road mobility.

中文翻译：

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城市道路中异构交通参与者的交互行为预测：基于图神经网络的多任务学习框架

有效预测城市道路交通参与者的交互行为是智能车辆成功决策和运动规划的关键。在本文中，基于从车载传感器收集的数据，提出了一种基于图神经网络的多任务学习框架（GNN-MTLF）来准确预测具有交互行为的交通参与者的轨迹。本研究中考虑的交互行为包括使用 GNN 建模为时空图的交互事件和轨迹。在 GNN-MTLF 下，预测过程包含两个主要部分：交互事件的识别和交互轨迹的预测。一个集成的损失函数是为多任务学习设计的，目的是预测和识别。所提出的框架使用城市道路中的自然驾驶数据进行了验证。实验结果表明，与基线方法相比，GNN-MTLF 具有更好的性能，并且具有提高道路机动性的潜力。