  Autonomous vehicles (AVs) are becoming an indispensable part of future
transportation. However, safety challenges and lack of reliability limit their
real-world deployment. Towards boosting the appearance of AVs on the roads, the
interaction of AVs with pedestrians including "prediction of the pedestrian
crossing intention" deserves extensive research. This is a highly challenging
task as involves multiple non-linear parameters. In this direction, we extract
and analyse spatio-temporal visual features of both pedestrian and traffic
contexts. The pedestrian features include body pose and local context features
that represent the pedestrian's behaviour. Additionally, to understand the
global context, we utilise location, motion, and environmental information
using scene parsing technology that represents the pedestrian's surroundings,
and may affect the pedestrian's intention. Finally, these multi-modality
features are intelligently fused for effective intention prediction learning.
The experimental results of the proposed model on the JAAD dataset show a
superior result on the combined AUC and F1-score compared to the
state-of-the-art.
