  Accurate pedestrian orientation estimation of autonomous driving helps the
ego vehicle obtain the intentions of pedestrians in the related environment,
which are the base of safety measures such as collision avoidance and
prewarning. However, because of relatively small sizes and high-level
deformation of pedestrians, common pedestrian orientation estimation models
fail to extract sufficient and comprehensive information from them, thus having
their performance restricted, especially monocular ones which fail to obtain
depth information of objects and related environment. In this paper, a novel
monocular pedestrian orientation estimation model, called FFNet, is proposed.
Apart from camera captures, the model adds the 2D and 3D dimensions of
pedestrians as two other inputs according to the logic relationship between
orientation and them. The 2D and 3D dimensions of pedestrians are determined
from the camera captures and further utilized through two feedforward links
connected to the orientation estimator. The feedforward links strengthen the
logicality and interpretability of the network structure of the proposed model.
Experiments show that the proposed model has at least 1.72% AOS increase than
most state-of-the-art models after identical training processes. The model also
has competitive results in orientation estimation evaluation on KITTI dataset.
