  Predicting vulnerable road user behavior is an essential prerequisite for
deploying Automated Driving Systems (ADS) in the real-world. Pedestrian
crossing intention should be recognized in real-time, especially for urban
driving. Recent works have shown the potential of using vision-based deep
neural network models for this task. However, these models are not robust and
certain issues still need to be resolved. First, the global spatio-temproal
context that accounts for the interaction between the target pedestrian and the
scene has not been properly utilized. Second, the optimum strategy for fusing
different sensor data has not been thoroughly investigated. This work addresses
the above limitations by introducing a novel neural network architecture to
fuse inherently different spatio-temporal features for pedestrian crossing
intention prediction. We fuse different phenomena such as sequences of RGB
imagery, semantic segmentation masks, and ego-vehicle speed in an optimum way
using attention mechanisms and a stack of recurrent neural networks. The
optimum architecture was obtained through exhaustive ablation and comparison
studies. Extensive comparative experiments on the JAAD pedestrian action
prediction benchmark demonstrate the effectiveness of the proposed method,
where state-of-the-art performance was achieved. Our code is open-source and
publicly available.
