# Evaluating Human Trajectory Prediction with Metamorphic Testing

arXiv: https://arxiv.org/abs/2407.18756 · HTML: https://mlsanity.com/paper/2407.18756/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 42 references that are in the map.
- First posted on arXiv 26 Jul 2024.

## Builds on (most cited first, 18 of 18)

- [Computational Optimal Transport](https://mlsanity.com/paper/1803.00567/) (2018)
- [Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction](https://mlsanity.com/paper/2002.11927/) (2020)
- [DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents](https://mlsanity.com/paper/1704.04394/) (2017)
- [It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction](https://mlsanity.com/paper/2004.02025/) (2020)
- [Machine Learning Testing: Survey, Landscapes and Horizons](https://mlsanity.com/paper/1906.10742/) (2019)
- [From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting](https://mlsanity.com/paper/2012.01526/) (2020)
- [On Adversarial Robustness of Trajectory Prediction for Autonomous Vehicles](https://mlsanity.com/paper/2201.05057/) (2022)
- [Metamorphic Testing: A New Approach for Generating Next Test Cases](https://mlsanity.com/paper/2002.12543/) (2020)
- [MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2108.09274/) (2021)
- [AdvDO: Realistic Adversarial Attacks for Trajectory Prediction](https://mlsanity.com/paper/2209.08744/) (2022)
- [Non-Probability Sampling Network for Stochastic Human Trajectory Prediction](https://mlsanity.com/paper/2203.13471/) (2022)
- [Disentangling Human Dynamics for Pedestrian Locomotion Forecasting with Noisy Supervision](https://mlsanity.com/paper/1911.01138/) (2019)
- [Targeted Adversarial Attacks against Neural Network Trajectory Predictors](https://mlsanity.com/paper/2212.04138/) (2022)
- [Evaluating Pedestrian Trajectory Prediction Methods with Respect to Autonomous Driving](https://mlsanity.com/paper/2308.05194/) (2023)
- [A Declarative Metamorphic Testing Framework for Autonomous Driving](https://mlsanity.com/paper/2012.10672/) (2020)
- [TrajPAC: Towards Robustness Verification of Pedestrian Trajectory Prediction Models](https://mlsanity.com/paper/2308.05985/) (2023)
- [Adaptive Metamorphic Testing with Contextual Bandits](https://mlsanity.com/paper/1910.00262/) (2019)
- [SimAug: Learning Robust Representations from Simulation for Trajectory Prediction](https://mlsanity.com/paper/2004.02022/) (2020)

## Built on by (most cited first, 3 of 3)

- [Metamorphic Testing for Pose Estimation Systems](https://mlsanity.com/paper/2502.09460/) (2025)
- [Metamorphic Testing with the Rashomon Set: Explanation Faithfulness in Machine Learning](https://mlsanity.com/paper/2606.06056/) (2026)
- [Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies](https://mlsanity.com/paper/2607.07029/) (2026)

## Abstract (arXiv)

The prediction of human trajectories is important for planning in autonomous systems that act in the real world, e.g. automated driving or mobile robots. Human trajectory prediction is a noisy process, and no prediction does precisely match any future trajectory. It is therefore approached as a stochastic problem, where the goal is to minimise the error between the true and the predicted trajectory. In this work, we explore the application of metamorphic testing for human trajectory prediction. Metamorphic testing is designed to handle unclear or missing test oracles. It is well-designed for human trajectory prediction, where there is no clear criterion of correct or incorrect human behaviour. Metamorphic relations rely on transformations over source test cases and exploit invariants. A setting well-designed for human trajectory prediction where there are many symmetries of expected human behaviour under variations of the input, e.g. mirroring and rescaling of the input data. We discuss how metamorphic testing can be applied to stochastic human trajectory prediction and introduce the Wasserstein Violation Criterion to statistically assess whether a follow-up test case violates a label-preserving metamorphic relation.