# Long-term Pedestrian Trajectory Prediction using Mutable Intention Filter and Warp LSTM

arXiv: https://arxiv.org/abs/2007.00113 · HTML: https://mlsanity.com/paper/2007.00113/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2020.

## Built on by (most cited first, 10 of 10)

- [Behavioral Intention Prediction in Driving Scenes: A Survey](https://mlsanity.com/paper/2211.00385/) (2022)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [Learning Sparse Interaction Graphs of Partially Detected Pedestrians for Trajectory Prediction](https://mlsanity.com/paper/2107.07056/) (2021)
- [Meta-path Analysis on Spatio-Temporal Graphs for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2202.13427/) (2022)
- [PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Systems](https://mlsanity.com/paper/2308.00864/) (2023)
- [LIT: Large Language Model Driven Intention Tracking for Proactive Human-Robot Collaboration -- A Robot Sous-Chef Application](https://mlsanity.com/paper/2406.13787/) (2024)
- [SGNetPose+: Stepwise Goal-Driven Networks with Pose Information for Trajectory Prediction in Autonomous Driving](https://mlsanity.com/paper/2503.08016/) (2025)
- [Intention-Aware Decision-Making for Mixed Intersection Scenarios](https://mlsanity.com/paper/2303.17493/) (2023)
- [Semantic and Articulated Pedestrian Sensing Onboard a Moving Vehicle](https://mlsanity.com/paper/2309.06313/) (2023)
- [Destination Support Restoration for Finite-Set Multimodal Trajectory Prediction](https://mlsanity.com/paper/2609.25942/) (2026)

## Abstract (arXiv)

Trajectory prediction is one of the key capabilities for robots to safely navigate and interact with pedestrians. Critical insights from human intention and behavioral patterns need to be integrated to effectively forecast long-term pedestrian behavior. Thus, we propose a framework incorporating a Mutable Intention Filter and a Warp LSTM (MIF-WLSTM) to simultaneously estimate human intention and perform trajectory prediction. The Mutable Intention Filter is inspired by particle filtering and genetic algorithms, where particles represent intention hypotheses that can be mutated throughout the pedestrian motion. Instead of predicting sequential displacement over time, our Warp LSTM learns to generate offsets on a full trajectory predicted by a nominal intention-aware linear model, which considers the intention hypotheses during filtering process. Through experiments on a publicly available dataset, we show that our method outperforms baseline approaches and demonstrate the robust performance of our method under abnormal intention-changing scenarios. Code is available at https://github.com/tedhuang96/mifwlstm.