# Modeling Cooperative Navigation in Dense Human Crowds

arXiv: https://arxiv.org/abs/1705.06201 · HTML: https://mlsanity.com/paper/1705.06201/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2017.

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

- [Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph](https://mlsanity.com/paper/2203.01821/) (2022)
- [Where to go next: Learning a Subgoal Recommendation Policy for Navigation Among Pedestrians](https://mlsanity.com/paper/2102.13073/) (2021)
- [Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation](https://mlsanity.com/paper/2401.12275/) (2024)
- [From Cognition to Precognition: A Future-Aware Framework for Social Navigation](https://mlsanity.com/paper/2409.13244/) (2024)
- [SocialCircle+: Learning the Angle-based Conditioned Interaction Representation for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2409.14984/) (2024)
- [Who Walks With You Matters: Perceiving Social Interactions with Groups for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2412.02395/) (2024)
- [Soft Attention: Does it Actually Help to Learn Social Interactions in Pedestrian Trajectory Prediction?](https://mlsanity.com/paper/2106.15321/) (2021)
- [Why Did This Model Forecast This Future? Closed-Form Temporal Saliency Towards Causal Explanations of Probabilistic Forecasts](https://mlsanity.com/paper/2206.00679/) (2022)
- [Tactile Aware Dynamic Obstacle Avoidance in Crowded Environment with Deep Reinforcement Learning](https://mlsanity.com/paper/2406.13434/) (2024)
- [Learning Priors of Human Motion With Vision Transformers](https://mlsanity.com/paper/2501.18543/) (2025)

## Abstract (arXiv)

For robots to be a part of our daily life, they need to be able to navigate among crowds not only safely but also in a socially compliant fashion. This is a challenging problem because humans tend to navigate by implicitly cooperating with one another to avoid collisions, while heading toward their respective destinations. Previous approaches have used hand-crafted functions based on proximity to model human-human and human-robot interactions. However, these approaches can only model simple interactions and fail to generalize for complex crowded settings. In this paper, we develop an approach that models the joint distribution over future trajectories of all interacting agents in the crowd, through a local interaction model that we train using real human trajectory data. The interaction model infers the velocity of each agent based on the spatial orientation of other agents in his vicinity. During prediction, our approach infers the goal of the agent from its past trajectory and uses the learned model to predict its future trajectory. We demonstrate the performance of our method against a state-of-the-art approach on a public dataset and show that our model outperforms when predicting future trajectories for longer horizons.