# TrajGATFormer: A Graph-Based Transformer Approach for Worker and Obstacle Trajectory Prediction in Off-site Construction Environments

arXiv: https://arxiv.org/abs/2510.22205 · HTML: https://mlsanity.com/paper/2510.22205/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 49 references that are in the map.
- First posted on arXiv 25 Oct 2025.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Self-Attention with Relative Position Representations](https://mlsanity.com/paper/1803.02155/) (2018)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [YOLOv10: Real-Time End-to-End Object Detection](https://mlsanity.com/paper/2405.14458/) (2024)
- [AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting](https://mlsanity.com/paper/2103.14023/) (2021)
- [Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks](https://mlsanity.com/paper/1907.03395/) (2019)
- [Transformer Networks for Trajectory Forecasting](https://mlsanity.com/paper/2003.08111/) (2020)
- [Hyperbolic Attention Networks](https://mlsanity.com/paper/1805.09786/) (2018)
- [What the Constant Velocity Model Can Teach Us About Pedestrian Motion Prediction](https://mlsanity.com/paper/1903.07933/) (2019)
- [Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approaches](https://mlsanity.com/paper/2111.06740/) (2021)
- [Under the Hood of Transformer Networks for Trajectory Forecasting](https://mlsanity.com/paper/2203.11878/) (2022)
- [MixNet: Structured Deep Neural Motion Prediction for Autonomous Racing](https://mlsanity.com/paper/2208.01862/) (2022)

## Built on by (most cited first, 2 of 2)

- [FinInvest-GTCN: Explainable Graph-Temporal-Causal Modeling for Risk-Aware Investment Decision Optimization](https://mlsanity.com/paper/2606.28933/) (2026)
- [Investigating Spatiotemporal Redundancy in Video Transformer for Collision Anticipation](https://mlsanity.com/paper/2610.04727/) (2026)

## Abstract (arXiv)

As the demand grows within the construction industry for processes that are not only faster but also safer and more efficient, offsite construction has emerged as a solution, though it brings new safety risks due to the close interaction between workers, machinery, and moving obstacles. Predicting the future trajectories of workers and taking into account social and environmental factors is a crucial step for developing collision-avoidance systems to mitigate such risks. Traditional methods often struggle to adapt to the dynamic and unpredictable nature of construction environments. Many rely on simplified assumptions or require hand-crafted features, limiting their ability to respond to complex, real-time interactions between workers and moving obstacles. While recent data-driven methods have improved the modeling of temporal patterns, they still face challenges in capturing long-term behavior and accounting for the spatial and social context crucial to collision risk assessment. To address these limitations, this paper proposes a framework integrating YOLOv10n and DeepSORT for precise detection and tracking, along with two novel trajectory prediction models: TrajGATFormer and TrajGATFormer-Obstacle. YOLOv10n serves as the backbone for object detection, accurately identifying workers and obstacles in diverse scenes, while DeepSORT efficiently tracks them over time with unique IDs for continuity. Both models employ a transformer encoder-decoder with Graph Attention Networks (GAT) to capture temporal and spatial interactions. TrajGATFormer predicts worker trajectories with an ADE of 1.25 m and FDE of 2.3 m over a 4.8 s horizon, while TrajGATFormer-Obstacle extends prediction to both workers and obstacles, achieving higher accuracy (ADE 1.15 m, FDE 2.2 m). Comparative analysis shows both models outperform traditional methods, reducing ADE and FDE by up to 35% and 38%, respectively.