  Understanding the behaviors and intentions of pedestrians is still one of the
main challenges for vehicle autonomy, as accurate predictions of their
intentions can guarantee their safety and driving comfort of vehicles. In this
paper, we address pedestrian crossing prediction in urban traffic environments
by linking the dynamics of a pedestrian's skeleton to a binary crossing
intention. We introduce TrouSPI-Net: a context-free, lightweight, multi-branch
predictor. TrouSPI-Net extracts spatio-temporal features for different time
resolutions by encoding pseudo-images sequences of skeletal joints' positions
and processes them with parallel attention modules and atrous convolutions. The
proposed approach is then enhanced by processing features such as relative
distances of skeletal joints, bounding box positions, or ego-vehicle speed with
U-GRUs. Using the newly proposed evaluation procedures for two large public
naturalistic data sets for studying pedestrian behavior in traffic: JAAD and
PIE, we evaluate TrouSPI-Net and analyze its performance. Experimental results
show that TrouSPI-Net achieved 0.76 F1 score on JAAD and 0.80 F1 score on PIE,
therefore outperforming current state-of-the-art while being lightweight and
context-free.
