Synthetic Data Generation Framework, Dataset, and Efficient Deep Model for Pedestrian Intention Prediction

Muhammad Naveed Riaz; Maciej Wielgosz; Abel Garcia Romera; Antonio M. Lopez

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arXiv · 2401.06757

Synthetic Data Generation Framework, Dataset, and Efficient Deep Model for Pedestrian Intention Prediction

Muhammad Naveed Riaz·Maciej Wielgosz·Abel Garcia Romera·Antonio M. Lopez

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Abstract

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.

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Muhammad Naveed Riaz, Maciej Wielgosz, Abel Garcia Romera, Antonio M. Lopez. 2024-01-12. Synthetic Data Generation Framework, Dataset, and Efficient Deep Model for Pedestrian Intention Prediction. https://arxiv.org/abs/2401.06757

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