论文标题
审查：具有异质图的深度学习框架用于互动感知轨迹预测
ReCoG: A Deep Learning Framework with Heterogeneous Graph for Interaction-Aware Trajectory Prediction
论文作者
论文摘要
预测周围车辆的未来轨迹对于在复杂的现实驾驶场景中自动驾驶汽车的导航至关重要。由于车辆的运动受到许多因素的影响，包括其周围的基础设施和车辆，这是具有挑战性的。在这项工作中，我们开发了复发（经常性卷积和图神经网络），该方案代表了与基础架构信息作为异质图的车辆相互作用，并应用图形神经网络（GNNS）来对轨迹预测的高级相互作用进行建模。图中的节点包含相应的特征，其中车辆节点包含其使用复发神经网络（RNN）编码的顺序特征，而基础架构节点包含使用卷积神经网络（CNN）编码的空间特征。然后，该杂志通过共同考虑所有功能来预测目标车辆的未来轨迹。实验是通过使用交互数据集进行的。实验结果表明，所提出的补充在不同类型的位移误差方面胜过其他最新方法，从而验证了开发方法的可行性和有效性。
Predicting the future trajectory of surrounding vehicles is essential for the navigation of autonomous vehicles in complex real-world driving scenarios. It is challenging as a vehicle's motion is affected by many factors, including its surrounding infrastructures and vehicles. In this work, we develop the ReCoG (Recurrent Convolutional and Graph Neural Networks), which is a general scheme that represents vehicle interactions with infrastructure information as a heterogeneous graph and applies graph neural networks (GNNs) to model the high-level interactions for trajectory prediction. Nodes in the graph contain corresponding features, where a vehicle node contains its sequential feature encoded using Recurrent Neural Network (RNN), and an infrastructure node contains spatial feature encoded using Convolutional Neural Network (CNN). Then the ReCoG predicts the future trajectory of the target vehicle by jointly considering all of the features. Experiments are conducted by using the INTERACTION dataset. Experimental results show that the proposed ReCoG outperforms other state-of-the-art methods in terms of different types of displacement error, validating the feasibility and effectiveness of the developed approach.
