  Accurate identification of important objects in the scene is a prerequisite
for safe and high-quality decision making and motion planning of intelligent
agents (e.g., autonomous vehicles) that navigate in complex and dynamic
environments. Most existing approaches attempt to employ attention mechanisms
to learn importance weights associated with each object indirectly via various
tasks (e.g., trajectory prediction), which do not enforce direct supervision on
the importance estimation. In contrast, we tackle this task in an explicit way
and formulate it as a binary classification ("important" or "unimportant")
problem. We propose a novel approach for important object identification in
egocentric driving scenarios with relational reasoning on the objects in the
scene. Besides, since human annotations are limited and expensive to obtain, we
present a semi-supervised learning pipeline to enable the model to learn from
unlimited unlabeled data. Moreover, we propose to leverage the auxiliary tasks
of ego vehicle behavior prediction to further improve the accuracy of
importance estimation. The proposed approach is evaluated on a public
egocentric driving dataset (H3D) collected in complex traffic scenarios. A
detailed ablative study is conducted to demonstrate the effectiveness of each
model component and the training strategy. Our approach also outperforms
rule-based baselines by a large margin.
