  Accurate pedestrian intention prediction (PIP) by Autonomous Vehicles (AVs)
is one of the current research challenges in this field. In this article, we
introduce PIP-Net, a novel framework designed to predict pedestrian crossing
intentions by AVs in real-world urban scenarios. We offer two variants of
PIP-Net designed for different camera mounts and setups. Leveraging both
kinematic data and spatial features from the driving scene, the proposed model
employs a recurrent and temporal attention-based solution, outperforming
state-of-the-art performance. To enhance the visual representation of road
users and their proximity to the ego vehicle, we introduce a categorical depth
feature map, combined with a local motion flow feature, providing rich insights
into the scene dynamics. Additionally, we explore the impact of expanding the
camera's field of view, from one to three cameras surrounding the ego vehicle,
leading to enhancement in the model's contextual perception. Depending on the
traffic scenario and road environment, the model excels in predicting
pedestrian crossing intentions up to 4 seconds in advance which is a
breakthrough in current research studies in pedestrian intention prediction.
Finally, for the first time, we present the Urban-PIP dataset, a customised
pedestrian intention prediction dataset, with multi-camera annotations in
real-world automated driving scenarios.
