Synthetic Data Generation Framework, Dataset, and Efficient Deep Model
  for Pedestrian Intention Prediction
Muhammad Naveed Riaz Maciej Wielgosz Abel Garcia Romera Antonio M. Lopez
Pedestrian intention prediction is crucial for autonomous driving. In
particular, knowing if pedestrians are going to cross in front of the
ego-vehicle is core to performing safe and comfortable maneuvers. Creating
accurate and fast models that predict such intentions from sequential images is
challenging. A factor contributing to this is the lack of datasets with diverse
crossing and non-crossing (C/NC) scenarios. We address this scarceness by
introducing a framework, named ARCANE, which allows programmatically generating
synthetic datasets consisting of C/NC video clip samples. As an example, we use
ARCANE to generate a large and diverse dataset named PedSynth. We will show how
PedSynth complements widely used real-world datasets such as JAAD and PIE, so
enabling more accurate models for C/NC prediction. Considering the onboard
deployment of C/NC prediction models, we also propose a deep model named
PedGNN, which is fast and has a very low memory footprint. PedGNN is based on a
GNN-GRU architecture that takes a sequence of pedestrian skeletons as input to
predict crossing intentions.