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

arXiv: https://arxiv.org/abs/2401.06757 · HTML: https://mlsanity.com/paper/2401.06757/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 42 references that are in the map.
- First posted on arXiv 12 Jan 2024.

## Builds on (most cited first, 21 of 21)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Playing for Data: Ground Truth from Computer Games](https://mlsanity.com/paper/1608.02192/) (2016)
- [Deep Visual Domain Adaptation: A Survey](https://mlsanity.com/paper/1802.03601/) (2018)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [RMPE: Regional Multi-person Pose Estimation](https://mlsanity.com/paper/1612.00137/) (2016)
- [AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time](https://mlsanity.com/paper/2211.03375/) (2022)
- [Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing](https://mlsanity.com/paper/1810.08705/) (2018)
- [Predicting Pedestrian Crossing Intention with Feature Fusion and Spatio-Temporal Attention](https://mlsanity.com/paper/2104.05485/) (2021)
- [PedX: Benchmark Dataset for Metric 3D Pose Estimation of Pedestrians in Complex Urban Intersections](https://mlsanity.com/paper/1809.03605/) (2018)
- [Semantic Image Segmentation: Two Decades of Research](https://mlsanity.com/paper/2302.06378/) (2023)
- [Is the Pedestrian going to Cross? Answering by 2D Pose Estimation](https://mlsanity.com/paper/1807.10580/) (2018)
- [Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Supervision](https://mlsanity.com/paper/2103.12209/) (2021)
- [Is attention to bounding boxes all you need for pedestrian action prediction?](https://mlsanity.com/paper/2107.08031/) (2021)
- [FuSSI-Net: Fusion of Spatio-temporal Skeletons for Intention Prediction Network](https://mlsanity.com/paper/2005.07796/) (2020)
- [Pedestrian Intention Prediction: A Multi-task Perspective](https://mlsanity.com/paper/2010.10270/) (2020)
- [TrouSPI-Net: Spatio-temporal attention on parallel atrous convolutions and U-GRUs for skeletal pedestrian crossing prediction](https://mlsanity.com/paper/2109.00953/) (2021)
- [Deep Virtual-to-Real Distillation for Pedestrian Crossing Prediction](https://mlsanity.com/paper/2211.00856/) (2022)
- [CARLA-BSP: a simulated dataset with pedestrians](https://mlsanity.com/paper/2305.00204/) (2023)

## Built on by (most cited first, 5 of 5)

- [GTransPDM: A Graph-embedded Transformer with Positional Decoupling for Pedestrian Crossing Intention Prediction](https://mlsanity.com/paper/2409.20223/) (2024)
- [Pedestrian intention prediction in Adverse Weather Conditions with Spiking Neural Networks and Dynamic Vision Sensors](https://mlsanity.com/paper/2406.00473/) (2024)
- [Gating Syn-to-Real Knowledge for Pedestrian Crossing Prediction in Safe Driving](https://mlsanity.com/paper/2409.06707/) (2024)
- [ARCANE-PedSynth: Synthetic Multi-Pedestrian Datasets with Behavioural Crossing Annotations](https://mlsanity.com/paper/2605.24950/) (2026)
- [Unified Vision-Centric Pedestrian Crossing Action Prediction via Adaptive Patch Projection and Proactive Spatial Rectification](https://mlsanity.com/paper/2609.07420/) (2026)

## Abstract (arXiv)

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.