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.

翻译：驾驶道路车辆的一个关键方面是与其他道路使用者进行交互，评估他们的意图，并做出风险感知的战术决策。实现智能自动驾驶系统的一个直观方法是融入人类驾驶行为的某些方面。为此，我们提出一种基于时空交通图的自车视角新型驾驶框架。该交通图不仅通过时间关联的消息传递建模道路使用者之间的空间交互，还建模他们各自的意图。我们利用时空图卷积网络（ST-GCN）来训练图边。这些边使用道路代理的三维位置和场景感知外观特征的参数化函数进行公式化。除了战术行为预测外，评估所提出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任务（尤其是在识别行人和骑行者等脆弱交互对象方面）来学习风险感知表征。