论文标题
使用流量场景图基于关系的运动预测
Relation-based Motion Prediction using Traffic Scene Graphs
论文作者
论文摘要
代表交通现场的相关信息和了解其环境对于自动驾驶的成功至关重要。在先前的工作中，几乎不考虑使用语义关系对自动驾驶汽车的周围围绕周围的自动驾驶汽车，即不同的交通参与者如何在基于交通规则的行为的背景下进行建模。这源于这样一个事实，即这些关系很难从现实世界的交通场景中提取。在这项工作中，我们以空间语义场景图的形式对流量场景进行建模，以进行有关交通参与者的各种不同预测，例如加速度和减速。我们的学习和推理方法使用图形神经网络（GNN），并表明合并有关交通参与者之间空间语义关系的明确信息可改善预测结果。具体而言，与基线相比，交通参与者的加速预测可提高12％，而基线并未利用此明确的信息。此外，通过包括有关以前场景的其他信息，我们取得了73％的改进。
Representing relevant information of a traffic scene and understanding its environment is crucial for the success of autonomous driving. Modeling the surrounding of an autonomous car using semantic relations, i.e., how different traffic participants relate in the context of traffic rule based behaviors, is hardly been considered in previous work. This stems from the fact that these relations are hard to extract from real-world traffic scenes. In this work, we model traffic scenes in a form of spatial semantic scene graphs for various different predictions about the traffic participants, e.g., acceleration and deceleration. Our learning and inference approach uses Graph Neural Networks (GNNs) and shows that incorporating explicit information about the spatial semantic relations between traffic participants improves the predicdtion results. Specifically, the acceleration prediction of traffic participants is improved by up to 12% compared to the baselines, which do not exploit this explicit information. Furthermore, by including additional information about previous scenes, we achieve 73% improvements.
