论文标题
RAIST：通过时空图形卷积网络学习风险意识交流
RAIST: Learning Risk Aware Traffic Interactions via Spatio-Temporal Graph Convolutional Networks
论文作者
论文摘要
驾驶公路车辆的一个关键方面是与其他道路使用者互动，评估他们的意图并做出风险意识的战术决策。实现智能自动驾驶系统的直观方法是结合人类驾驶行为的某些方面。为此，我们提出了一个基于时空交通图的以自我为中心视图的新型驾驶框架。交通图不仅模型道路用户之间的空间交互，而且还通过时间关联的消息传递来模型。我们利用时空图卷积网络（ST-GCN）来训练图形边缘。这些边缘是使用3D位置的参数化函数和公路代理的场景感知外观特征来制定的。除了战术行为预测外，评估提议框架的风险评估能力至关重要。我们声称，我们的框架通过改进风险对象识别的任务来学习风险感知的表示形式，尤其是在识别具有行人和骑自行车者等脆弱互动的对象时。
A key aspect of driving a road vehicle is to interact with other road users, assess their intentions and make risk-aware tactical decisions. An intuitive approach to enabling an intelligent automated driving system would be incorporating some aspects of human driving behavior. To this end, we propose a novel driving framework for egocentric views based on spatio-temporal traffic graphs. The traffic graphs model not only the spatial interactions amongst the road users but also their individual intentions through temporally associated message passing. We leverage a spatio-temporal graph convolutional network (ST-GCN) to train the graph edges. These edges are formulated using parameterized functions of 3D positions and scene-aware appearance features of road agents. Along with tactical behavior prediction, it is crucial to evaluate the risk-assessing ability of the proposed framework. We claim that our framework learns risk-aware representations by improving on the task of risk object identification, especially in identifying objects with vulnerable interactions like pedestrians and cyclists.
