Pose-Based Pedestrian Crossing Intention Prediction with Recurrent Graph Convolutions and Explainability-Driven Data Augmentation
This paper presents three contributions to the field of autonomous driving. Firstly, we present a Graph Neural Network designed for the pedestrian crossing intention prediction. The proposed architecture significantly outperforms the state-of-the-art methods in terms of f1-score, precision, and reca...
| Autor: | |
|---|---|
| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:293067 |
| Acceso en línea: | https://ddd.uab.cat/record/293067 |
| Access Level: | acceso abierto |
| Palabra clave: | Advanced Driver Assistance Systems Data augmentation Explainability Graph Neural Networks Pedestrian Crossing Intention |
| Sumario: | This paper presents three contributions to the field of autonomous driving. Firstly, we present a Graph Neural Network designed for the pedestrian crossing intention prediction. The proposed architecture significantly outperforms the state-of-the-art methods in terms of f1-score, precision, and recall. The input consists of the skeletons of the pedestrians. It extracts features from the skeletons using a Recurrent Graph Convolutional Layer that captures both the spatial and temporal dependencies. Secondly, an explainability analysis has been performed using our implementation of Grad-Cam. Finally, based on the results of the explainability, we have proposed a data augmentation method that generates new skeletons. |
|---|