  In order to be globally deployed, autonomous cars must guarantee the safety
of pedestrians. This is the reason why forecasting pedestrians' intentions
sufficiently in advance is one of the most critical and challenging tasks for
autonomous vehicles. This work tries to solve this problem by jointly
predicting the intention and visual states of pedestrians. In terms of visual
states, whereas previous work focused on x-y coordinates, we will also predict
the size and indeed the whole bounding box of the pedestrian. The method is a
recurrent neural network in a multi-task learning approach. It has one head
that predicts the intention of the pedestrian for each one of its future
position and another one predicting the visual states of the pedestrian.
Experiments on the JAAD dataset show the superiority of the performance of our
method compared to previous works for intention prediction. Also, although its
simple architecture (more than 2 times faster), the performance of the bounding
box prediction is comparable to the ones yielded by much more complex
architectures. Our code is available online.
