# Predicting Pedestrian Crossing Behavior in Germany and Japan: Insights into Model Transferability

arXiv: https://arxiv.org/abs/2412.03689 · HTML: https://mlsanity.com/paper/2412.03689/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 49 references that are in the map.
- First posted on arXiv 4 Dec 2024.

## Builds on (most cited first, 10 of 10)

- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction](https://mlsanity.com/paper/2002.11927/) (2020)
- [Autonomous Vehicles that Interact with Pedestrians: A Survey of Theory and Practice](https://mlsanity.com/paper/1805.11773/) (2018)
- [Is the Pedestrian going to Cross? Answering by 2D Pose Estimation](https://mlsanity.com/paper/1807.10580/) (2018)
- [Looking Ahead: Anticipating Pedestrians Crossing with Future Frames Prediction](https://mlsanity.com/paper/1910.09077/) (2019)
- [Social-IWSTCNN: A Social Interaction-Weighted Spatio-Temporal Convolutional Neural Network for Pedestrian Trajectory Prediction in Urban Traffic Scenarios](https://mlsanity.com/paper/2105.12436/) (2021)
- [Learning the Pedestrian-Vehicle Interaction for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2202.05334/) (2022)
- [Analysis and Prediction of Pedestrian Crosswalk Behavior during Automated Vehicle Interactions](https://mlsanity.com/paper/2003.09996/) (2020)
- [Cross or Wait? Predicting Pedestrian Interaction Outcomes at Unsignalized Crossings](https://mlsanity.com/paper/2304.08260/) (2023)
- [Predicting and Analyzing Pedestrian Crossing Behavior at Unsignalized Crossings](https://mlsanity.com/paper/2404.09574/) (2024)

## Abstract (arXiv)

Predicting pedestrian crossing behavior is important for intelligent traffic systems to avoid pedestrian-vehicle collisions. Most existing pedestrian crossing behavior models are trained and evaluated on datasets collected from a single country, overlooking differences between countries. To address this gap, we compared pedestrian road-crossing behavior at unsignalized crossings in Germany and Japan. We presented four types of machine learning models to predict gap selection behavior, zebra crossing usage, and their trajectories using simulator data collected from both countries. When comparing the differences between countries, pedestrians from the study conducted in Japan are more cautious, selecting larger gaps compared to those in Germany. We evaluate and analyze model transferability. Our results show that neural networks outperform other machine learning models in predicting gap selection and zebra crossing usage, while random forest models perform best on trajectory prediction tasks, demonstrating strong performance and transferability. We develop a transferable model using an unsupervised clustering method, which improves prediction accuracy for gap selection and trajectory prediction. These findings provide a deeper understanding of pedestrian crossing behaviors in different countries and offer valuable insights into model transferability.