  One of the most crucial yet challenging tasks for autonomous vehicles in
urban environments is predicting the future behaviour of nearby pedestrians,
especially at points of crossing. Predicting behaviour depends on many social
and environmental factors, particularly interactions between road users.
Capturing such interactions requires a global view of the scene and dynamics of
the road users in three-dimensional space. This information, however, is
missing from the current pedestrian behaviour benchmark datasets. Motivated by
these challenges, we propose 1) a novel graph-based model for predicting
pedestrian crossing action. Our method models pedestrians' interactions with
nearby road users through clustering and relative importance weighting of
interactions using features obtained from the bird's-eye-view. 2) We introduce
a new dataset that provides 3D bounding box and pedestrian behavioural
annotations for the existing nuScenes dataset. On the new data, our approach
achieves state-of-the-art performance by improving on various metrics by more
than 15% in comparison to existing methods. The dataset is available at
https://github.com/huawei-noah/datasets/PePScenes.
