  To enable intelligent automated driving systems, a promising strategy is to
understand how human drives and interacts with road users in complicated
driving situations. In this paper, we propose a 3D-aware egocentric
spatial-temporal interaction framework for automated driving applications.
Graph convolution networks (GCN) is devised for interaction modeling. We
introduce three novel concepts into GCN. First, we decompose egocentric
interactions into ego-thing and ego-stuff interaction, modeled by two GCNs. In
both GCNs, ego nodes are introduced to encode the interaction between thing
objects (e.g., car and pedestrian), and interaction between stuff objects
(e.g., lane marking and traffic light). Second, objects' 3D locations are
explicitly incorporated into GCN to better model egocentric interactions.
Third, to implement ego-stuff interaction in GCN, we propose a MaskAlign
operation to extract features for irregular objects.
  We validate the proposed framework on tactical driver behavior recognition.
Extensive experiments are conducted using Honda Research Institute Driving
Dataset, the largest dataset with diverse tactical driver behavior annotations.
Our framework demonstrates substantial performance boost over baselines on the
two experimental settings by 3.9% and 6.0%, respectively. Furthermore, we
visualize the learned affinity matrices, which encode ego-thing and ego-stuff
interactions, to showcase the proposed framework can capture interactions
effectively.
