  With the unprecedented shift towards automated urban environments in recent
years, a new paradigm is required to study pedestrian behaviour. Studying
pedestrian behaviour in futuristic scenarios requires modern data sources that
consider both the Automated Vehicle (AV) and pedestrian perspectives. Current
open datasets on AVs predominantly fail to account for the latter, as they do
not include an adequate number of events and associated details that involve
pedestrian and vehicle interactions. To address this issue, we propose using
Virtual Reality (VR) data as a complementary resource to current datasets,
which can be designed to measure pedestrian behaviour under specific
conditions. In this research, we focus on the context-aware pedestrian
trajectory prediction framework for automated vehicles at mid-block
unsignalized crossings. For this purpose, we develop a novel multi-input
network of Long Short-Term Memory (LSTM) and fully connected dense layers. In
addition to past trajectories, the proposed framework incorporates pedestrian
head orientations and distance to the upcoming vehicles as sequential input
data. By merging the sequential data with contextual information of the
environment, we train a model to predict the future pedestrian trajectory. Our
results show that the prediction error and overfitting to the training data are
reduced by considering contextual information in the model. To analyze the
application of the methods to real AV data, the proposed framework is trained
and applied to pedestrian trajectory extracted from an open-access video
dataset. Finally, by implementing a game theory-based model interpretability
method, we provide detailed insights and propose recommendations to improve the
current automated vehicle sensing systems from a pedestrian-oriented point of
view.
