  Existing deep-learning based monocular orientation estimation algorithms
faces the problem of confusion between the anterior and posterior parts of the
objects, caused by the feature similarity of such parts in typical objects in
traffic scenes such as cars and pedestrians. While difficult to solve, the
problem may lead to serious orientation estimation errors, and pose threats to
the upcoming decision making process of the ego vehicle, since the predicted
tracks of objects may have directions opposite to ground truths. In this paper,
we mitigate this problem by proposing a pretraining method. The method focuses
on predicting the left/right semicircle in which the orientation of the object
is located. The trained semicircle prediction model is then integrated into the
orientation angle estimation model which predicts a value in range $[0, \pi]$.
Experiment results show that the proposed semicircle prediction enhances the
accuracy of orientation estimation, and mitigates the problem stated above.
With the proposed method, a backbone achieves similar state-of-the-art
orientation estimation performance to existing approaches with well-designed
network structures.
