# An Information-Theoretic Approach for Estimating Scenario Generalization in Crowd Motion Prediction

arXiv: https://arxiv.org/abs/2211.00817 · HTML: https://mlsanity.com/paper/2211.00817/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 45 references that are in the map.
- First posted on arXiv 2 Nov 2022.

## Builds on (most cited first, 13 of 13)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction](https://mlsanity.com/paper/2002.11927/) (2020)
- [Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks](https://mlsanity.com/paper/1906.08291/) (2019)
- [Computing the Testing Error without a Testing Set](https://mlsanity.com/paper/2005.00450/) (2020)
- [OpenTraj: Assessing Prediction Complexity in Human Trajectories Datasets](https://mlsanity.com/paper/2010.00890/) (2020)
- [Deep-Learned Collision Avoidance Policy for Distributed Multi-Agent Navigation](https://mlsanity.com/paper/1609.06838/) (2016)
- [Interaction-aware Multi-agent Tracking and Probabilistic Behavior Prediction via Adversarial Learning](https://mlsanity.com/paper/1904.02390/) (2019)
- [A survey on trajectory clustering analysis](https://mlsanity.com/paper/1802.06971/) (2018)
- [Informative Scene Decomposition for Crowd Analysis, Comparison and Simulation Guidance](https://mlsanity.com/paper/2004.14107/) (2020)
- [Scenario Generalization of Data-driven Imitation Models in Crowd Simulation](https://mlsanity.com/paper/1910.00738/) (2019)
- [A Perceptually-Validated Metric for Crowd Trajectory Quality Evaluation](https://mlsanity.com/paper/2108.12346/) (2021)
- [The Role of Data-driven Priors in Multi-agent Crowd Trajectory Estimation](https://mlsanity.com/paper/1710.03354/) (2017)

## Abstract (arXiv)

Learning-based approaches to modeling crowd motion have become increasingly successful but require training and evaluation on large datasets, coupled with complex model selection and parameter tuning. To circumvent this tremendously time-consuming process, we propose a novel scoring method, which characterizes generalization of models trained on source crowd scenarios and applied to target crowd scenarios using a training-free, model-agnostic Interaction + Diversity Quantification score, ISDQ. The Interaction component aims to characterize the difficulty of scenario domains, while the diversity of a scenario domain is captured in the Diversity score. Both scores can be computed in a computation tractable manner. Our experimental results validate the efficacy of the proposed method on several simulated and real-world (source,target) generalization tasks, demonstrating its potential to select optimal domain pairs before training and testing a model.