# FuSSI-Net: Fusion of Spatio-temporal Skeletons for Intention Prediction Network

arXiv: https://arxiv.org/abs/2005.07796 · HTML: https://mlsanity.com/paper/2005.07796/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2020.

## Built on by (most cited first, 17 of 17)

- [Predicting Pedestrian Crossing Intention with Feature Fusion and Spatio-Temporal Attention](https://mlsanity.com/paper/2104.05485/) (2021)
- [Behavioral Intention Prediction in Driving Scenes: A Survey](https://mlsanity.com/paper/2211.00385/) (2022)
- [IntFormer: Predicting pedestrian intention with the aid of the Transformer architecture](https://mlsanity.com/paper/2105.08647/) (2021)
- [TrouSPI-Net: Spatio-temporal attention on parallel atrous convolutions and U-GRUs for skeletal pedestrian crossing prediction](https://mlsanity.com/paper/2109.00953/) (2021)
- [Foul prediction with estimated poses from soccer broadcast video](https://mlsanity.com/paper/2402.09650/) (2024)
- [Synthetic Data Generation Framework, Dataset, and Efficient Deep Model for Pedestrian Intention Prediction](https://mlsanity.com/paper/2401.06757/) (2024)
- [WatchPed: Pedestrian Crossing Intention Prediction Using Embedded Sensors of Smartwatch](https://mlsanity.com/paper/2208.07441/) (2022)
- [Local and Global Contextual Features Fusion for Pedestrian Intention Prediction](https://mlsanity.com/paper/2305.01111/) (2023)
- [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)
- [SGNetPose+: Stepwise Goal-Driven Networks with Pose Information for Trajectory Prediction in Autonomous Driving](https://mlsanity.com/paper/2503.08016/) (2025)
- [Pedestrian Crossing Intention Prediction Using Multimodal Fusion Network](https://mlsanity.com/paper/2511.20008/) (2025)
- [Cause-and-Effect Analysis of ADAS: A Comparison Study between Literature Review and Complaint Data](https://mlsanity.com/paper/2208.00249/) (2022)
- [Semantic and Articulated Pedestrian Sensing Onboard a Moving Vehicle](https://mlsanity.com/paper/2309.06313/) (2023)
- [WTTFNet: A Weather-Time-Trajectory Fusion Network for Pedestrian Trajectory Prediction in Urban Complex](https://mlsanity.com/paper/2405.18945/) (2024)
- [Adaptive Cross-Modal Fusion with Sparse Attention for Pedestrian Crossing Intention Prediction](https://mlsanity.com/paper/2607.12293/) (2026)

## Abstract (arXiv)

Pedestrian intention recognition is very important to develop robust and safe autonomous driving (AD) and advanced driver assistance systems (ADAS) functionalities for urban driving. In this work, we develop an end-to-end pedestrian intention framework that performs well on day- and night- time scenarios. Our framework relies on objection detection bounding boxes combined with skeletal features of human pose. We study early, late, and combined (early and late) fusion mechanisms to exploit the skeletal features and reduce false positives as well to improve the intention prediction performance. The early fusion mechanism results in AP of 0.89 and precision/recall of 0.79/0.89 for pedestrian intention classification. Furthermore, we propose three new metrics to properly evaluate the pedestrian intention systems. Under these new evaluation metrics for the intention prediction, the proposed end-to-end network offers accurate pedestrian intention up to half a second ahead of the actual risky maneuver.