HRI MobiSim - Body - Honda Research Institute USA

HRI MobiSim - Body

Introduction

Driving simulators are a standard tool in human factors research, but most are tied to one laboratory. That limits sample size, the diversity of participants and how quickly scenarios can change. These limits matter more as mobility research moves beyond the driver and the vehicle to the many road users who now share streets and sidewalks.

HRI MobiSim addresses this with a single Unity project that builds to two targets: a WebGL version that runs in a web browser for large crowdsourced online studies, and a native VR version for in-lab studies with physical hardware and physiological sensors. Surveys, attention checks, condition assignment and continuous behavioral logging are built in, so a study can move from online screening to in-lab replication without rebuilding the scenario.

At a glance

  • Built in Unity. Scenarios are set up in the Unity Inspector, mostly without writing code.
  • The participant can be a car driver, e-scooter rider, wheelchair user, pedestrian or delivery robot.
  • Online studies run in the browser, with a lightweight PHP server that checks participant IDs and collects data. It works with Prolific and MTurk.
  • In-lab studies work with PC-based VR headsets, physical controls such as a scooter handlebar, and physiological sensors.
  • Used as the main apparatus in three peer-reviewed studies with 458 participants in total.
  • Available for non-commercial use.
Aerial view of the suburban city map used in HRI MobiSim, shown in the Unity Scene view
Mobility platforms, left to right: delivery robot, mobility vehicle, tri-wheeled scooter, seated and standing scooters, wheelchair.

Key features

Scenario testbed

A suburban city map with scripted pedestrians, riders, vehicles and robots that create yielding, overtaking, gap-acceptance and helping interactions.

Mobility platforms

A delivery robot, an automated mobility vehicle, a tri-wheeled scooter, seated and standing scooters, and a wheelchair, in first- or third-person view.

Flexible inputs

Keyboard and mouse for online studies; Logitech wheels, joysticks, Fanatec direct-drive bases and custom Arduino rigs in the lab.

Built-in assessments

Likert, slider, dropdown and free-text surveys, attention checks and a Stroop task, given before, during or after a ride in the same session.

Scene Graph

A visual editor for the study flow that assigns conditions and counterbalances by subject ID, for within-subjects, between-subjects and mixed designs.

Behavioral logging

Time-stamped JSON logs of participant input and scene state, from which speed, gap acceptance, time-to-collision and event-locked measures can be derived.

Six mobility platforms: delivery robot, mobility vehicle, tri-wheeled scooter, seated scooter, standing scooter and wheelchair
Mobility platforms, left to right: delivery robot, mobility vehicle, tri-wheeled scooter, seated and standing scooters, wheelchair.
Scene Graph editor with start, modulo and branch nodes routing participants to condition paths
The Scene Graph assigns participants to counterbalanced conditions.
Sample survey shown inside the simulator with text, dropdown, Likert and checkbox questions
Surveys run inside the simulation.

Deployment options

Browser version of HRI MobiSim showing route progress, timer, speed and a turn arrow over the rider view

Online, in the browser (WebGL)

Best for large and diverse samples.

  • Participants open a link from Prolific or MTurk in a desktop browser and use the keyboard.
  • A small PHP server validates each participant ID, blocks repeat sign-ups and stores the data.
A participant wearing a VR headset rides a physical scooter handlebar mounted on a steering base

In the lab, in virtual reality

Best for high-fidelity studies and physiological measures.

  • Works with PC-based VR headsets (tested with the Varjo XR-3). Our lab setup adds a physical scooter handlebar on a Fanatec base and a Shimmer3 wristband for heart rate and electrodermal activity.
  • Experimenters can trigger scripted events and code behavior live from a tablet Remote Control app.

Both versions are built from the same project, with the same scenarios, agents, Scene Graph and log format, so results can be compared directly across settings.

Published studies

HRI MobiSim has been the main experimental apparatus in these peer-reviewed studies:

Study Setting Participants What it examined
Scott-Sharoni et al., AutomotiveUI 2024 Online (WebGL, Prolific) 208 Whether post-ride feedback (a “Ride Report”) makes e-scooter riders more considerate on shared sidewalks
Kim et al., AutomotiveUI 2024 Online (WebGL, Prolific) 200 How considerate or inconsiderate pedestrian behavior affects riders’ decisions, safety margins and well-being under time pressure
Salubre et al., HFES Annual Meeting 2025 In-lab VR 50 Intentions to help other road users in micromobility, with heart rate and electrodermal activity

What’s included

  • Full Unity project source code
  • Server-side PHP scripts for participant authorization and data collection
  • Sample scenarios and ready-made actors
  • Online documentation, API reference and a browser demo (no installation or account needed)

System requirements

Operating system Windows 10 or 11 (64-bit)
Game engine Unity 2022.3 LTS
Graphics Graphics card with DirectX 10, 11 or 12 support
Online studies Unity WebGL Build Support module; a web server with PHP to host the build and collect data
VR studies (optional) Any PC-based VR headset (tested with the Varjo XR-3)
Input hardware (optional) Steering wheels, joysticks, Fanatec bases or custom devices through the Unity Input System

Citation

If you use HRI MobiSim in your research, please cite:

Salubre, K., Lo, H.-C., Akash, K., & Misu, T. (2026). Mobility Interaction Sim: A scalable experimental research platform for mobility research. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. https://doi.org/10.1177/10711813261485186

@article{salubre2026mobisim,
  title     = {Mobility Interaction Sim: A Scalable Experimental Research
               Platform for Mobility Research},
  author    = {Salubre, Kevin and Lo, Hiu-Chun and Akash, Kumar and Misu, Teruhisa},
  journal   = {Proceedings of the Human Factors and Ergonomics Society Annual Meeting},
  year      = {2026},
  publisher = {SAGE Publications},
  doi       = {10.1177/10711813261485186}
}

Download the software

The software is available for non-commercial usage. You must be affiliated with a university and use your university email address to make the request. Use this link to make the download request.

Want to try it first? The browser demo needs no installation or account.