# Pedestrian-Robot Interactions on Autonomous Crowd Navigation: Reactive Control Methods and Evaluation Metrics

arXiv: https://arxiv.org/abs/2208.02121 · HTML: https://mlsanity.com/paper/2208.02121/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 3 Aug 2022.

## Builds on (most cited first, 11 of 11)

- [Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning](https://mlsanity.com/paper/1809.08835/) (2018)
- [PORCA: Modeling and Planning for Autonomous Driving among Many Pedestrians](https://mlsanity.com/paper/1805.11833/) (2018)
- [DenseCAvoid: Real-time Navigation in Dense Crowds using Anticipatory Behaviors](https://mlsanity.com/paper/2002.03038/) (2020)
- [DR-SPAAM: A Spatial-Attention and Auto-regressive Model for Person Detection in 2D Range Data](https://mlsanity.com/paper/2004.14079/) (2020)
- [HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under Uncertainty](https://mlsanity.com/paper/1802.06215/) (2018)
- [Frozone: Freezing-Free, Pedestrian-Friendly Navigation in Human Crowds](https://mlsanity.com/paper/2003.05395/) (2020)
- [Crowd against the machine: A simulation-based benchmark tool to evaluate and compare robot capabilities to navigate a human crowd](https://mlsanity.com/paper/2104.14177/) (2021)
- [Person-MinkUNet: 3D Person Detection with LiDAR Point Cloud](https://mlsanity.com/paper/2107.06780/) (2021)
- [Unfreezing Social Navigation: Dynamical Systems based Compliance for Contact Control in Robot Navigation](https://mlsanity.com/paper/2203.01053/) (2022)
- [Personal Mobility With Synchronous Trunk-Knee Passive Exoskeleton: Optimizing Human-Robot Energy Transfer](https://mlsanity.com/paper/2201.03374/) (2022)
- [Control Interface for Hands-free Navigation of Standing Mobility Vehicles based on Upper-Body Natural Movements](https://mlsanity.com/paper/2008.01181/) (2020)

## Built on by (most cited first, 5 of 5)

- [TBD Pedestrian Data Collection: Towards Rich, Portable, and Large-Scale Natural Pedestrian Data](https://mlsanity.com/paper/2309.17187/) (2023)
- [Aion: Towards Hierarchical 4D Scene Graphs with Temporal Flow Dynamics](https://mlsanity.com/paper/2512.11903/) (2025)
- [Rheos: Modelling Continuous Motion Dynamics in Hierarchical 3D Scene Graphs](https://mlsanity.com/paper/2603.20239/) (2026)
- [Learning Contact-based Navigation in Crowds](https://mlsanity.com/paper/2303.01455/) (2023)
- [Shared Control of Holonomic Wheelchairs through Reinforcement Learning](https://mlsanity.com/paper/2507.17055/) (2025)

## Abstract (arXiv)

Autonomous navigation in highly populated areas remains a challenging task for robots because of the difficulty in guaranteeing safe interactions with pedestrians in unstructured situations. In this work, we present a crowd navigation control framework that delivers continuous obstacle avoidance and post-contact control evaluated on an autonomous personal mobility vehicle. We propose evaluation metrics for accounting efficiency, controller response and crowd interactions in natural crowds. We report the results of over 110 trials in different crowd types: sparse, flows, and mixed traffic, with low- (< 0.15 ppsm), mid- (< 0.65 ppsm), and high- (< 1 ppsm) pedestrian densities. We present comparative results between two low-level obstacle avoidance methods and a baseline of shared control. Results show a 10% drop in relative time to goal on the highest density tests, and no other efficiency metric decrease. Moreover, autonomous navigation showed to be comparable to shared-control navigation with a lower relative jerk and significantly higher fluency in commands indicating high compatibility with the crowd. We conclude that the reactive controller fulfils a necessary task of fast and continuous adaptation to crowd navigation, and it should be coupled with high-level planners for environmental and situational awareness.