  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.

Computer Vision and Pattern Recognition;Robotics