# IntFormer: Predicting pedestrian intention with the aid of the Transformer architecture

arXiv: https://arxiv.org/abs/2105.08647 · HTML: https://mlsanity.com/paper/2105.08647/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 18 May 2021.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [TSM: Temporal Shift Module for Efficient Video Understanding](https://mlsanity.com/paper/1811.08383/) (2018)
- [Human Motion Trajectory Prediction: A Survey](https://mlsanity.com/paper/1905.06113/) (2019)
- [A Comprehensive Study of Deep Video Action Recognition](https://mlsanity.com/paper/2012.06567/) (2020)
- [Real-time Intent Prediction of Pedestrians for Autonomous Ground Vehicles via Spatio-Temporal DenseNet](https://mlsanity.com/paper/1904.09862/) (2019)
- [Looking Ahead: Anticipating Pedestrians Crossing with Future Frames Prediction](https://mlsanity.com/paper/1910.09077/) (2019)
- [RNN-based Pedestrian Crossing Prediction using Activity and Pose-related Features](https://mlsanity.com/paper/2008.11647/) (2020)
- [FuSSI-Net: Fusion of Spatio-temporal Skeletons for Intention Prediction Network](https://mlsanity.com/paper/2005.07796/) (2020)
- [Joint Attention in Driver-Pedestrian Interaction: from Theory to Practice](https://mlsanity.com/paper/1802.02522/) (2018)

## Built on by (most cited first, 13 of 13)

- [Behavioral Intention Prediction in Driving Scenes: A Survey](https://mlsanity.com/paper/2211.00385/) (2022)
- [Assessing Cross-dataset Generalization of Pedestrian Crossing Predictors](https://mlsanity.com/paper/2201.12626/) (2022)
- [GTransPDM: A Graph-embedded Transformer with Positional Decoupling for Pedestrian Crossing Intention Prediction](https://mlsanity.com/paper/2409.20223/) (2024)
- [Multi-Context Fusion Transformer for Pedestrian Crossing Intention Prediction in Urban Environments](https://mlsanity.com/paper/2511.20011/) (2025)
- [ACIT: Attention-Guided Cross-Modal Interaction Transformer for Pedestrian Crossing Intention Prediction](https://mlsanity.com/paper/2511.20020/) (2025)
- [Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach](https://mlsanity.com/paper/2411.13302/) (2024)
- [Seeing Beyond Frames: Zero-Shot Pedestrian Intention Prediction with Raw Temporal Video and Multimodal Cues](https://mlsanity.com/paper/2507.21161/) (2025)
- [VIT-Ped: Visionary Intention Transformer for Pedestrian Behavior Analysis](https://mlsanity.com/paper/2601.01989/) (2026)
- [Gating Syn-to-Real Knowledge for Pedestrian Crossing Prediction in Safe Driving](https://mlsanity.com/paper/2409.06707/) (2024)
- [Pedestrian Crossing Intention Prediction Using Multimodal Fusion Network](https://mlsanity.com/paper/2511.20008/) (2025)
- [Temporal-contextual Event Learning for Pedestrian Crossing Intent Prediction](https://mlsanity.com/paper/2504.06292/) (2025)
- [Enhancing Customer Service Chatbots with Context-Aware NLU through Selective Attention and Multi-task Learning](https://mlsanity.com/paper/2506.01781/) (2025)
- [Adaptive Cross-Modal Fusion with Sparse Attention for Pedestrian Crossing Intention Prediction](https://mlsanity.com/paper/2607.12293/) (2026)

## Abstract (arXiv)

Understanding pedestrian crossing behavior is an essential goal in intelligent vehicle development, leading to an improvement in their security and traffic flow. In this paper, we developed a method called IntFormer. It is based on transformer architecture and a novel convolutional video classification model called RubiksNet. Following the evaluation procedure in a recent benchmark, we show that our model reaches state-of-the-art results with good performance (≈ 40 seq. per second) and size (8× smaller than the best performing model), making it suitable for real-time usage. We also explore each of the input features, finding that ego-vehicle speed is the most important variable, possibly due to the similarity in crossing cases in PIE dataset.