  Anticipating the intentions of vulnerable road users (VRUs) such as
pedestrians and cyclists is critical for performing safe and comfortable
driving maneuvers. This is the case for human driving and, thus, should be
taken into account by systems providing any level of driving assistance, from
advanced driver assistant systems (ADAS) to fully autonomous vehicles (AVs). In
this paper, we show how the latest advances on monocular vision-based human
pose estimation, i.e. those relying on deep Convolutional Neural Networks
(CNNs), enable to recognize the intentions of such VRUs. In the case of
cyclists, we assume that they follow traffic rules to indicate future maneuvers
with arm signals. In the case of pedestrians, no indications can be assumed.
Instead, we hypothesize that the walking pattern of a pedestrian allows to
determine if he/she has the intention of crossing the road in the path of the
ego-vehicle, so that the ego-vehicle must maneuver accordingly (e.g. slowing
down or stopping). In this paper, we show how the same methodology can be used
for recognizing pedestrians and cyclists' intentions. For pedestrians, we
perform experiments on the JAAD dataset. For cyclists, we did not found an
analogous dataset, thus, we created our own one by acquiring and annotating
videos which we share with the research community. Overall, the proposed
pipeline provides new state-of-the-art results on the intention recognition of
VRUs.
