JOAN
JOAN: a framework for human-automated vehicle interaction experiments in a virtual reality driving simulator - Published in JOSS (2023)
Science Score: 95.0%
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7 of 15 committers (46.7%) from academic institutions -
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Published in Journal of Open Source Software
Keywords
Scientific Fields
Repository
JOAN is an software package that allows to perform human-in-the loop experiments in the open source driving simulator CARLA. JOAN facilitates communication between human input devices and CARLA, the implementation of haptic feedback, systematically storing experiment data, and the automatic execution of experiments with multiple experimental conditions.
Basic Info
Statistics
- Stars: 15
- Watchers: 4
- Forks: 10
- Open Issues: 18
- Releases: 1
Topics
Metadata Files
README.md
JOAN
Introduction
To design automated vehicles (AVs) that can be safely introduced in our mixed traffic, research into human-AV interaction is needed. Driving simulators are invaluable tools, however, existing driving simulators are expensive, implementing new functionalities (e.g., AV control algorithms) can be difficult, and setting up new experiments can be tedious. To address these issues, we present JOAN, a framework for human-AV interaction experiments. JOAN connects to the open-source AV-simulation program Carla and enables quick and easy implementation of human-in-the-loop driving experiments. It supports experiments in VR, a variety of human input devices, and haptic feedback. JOAN is easy to use, flexible, and enables researchers to design, store, and perform human-factors experiments without writing a single line of code.
JOAN builts upon Carla, a open-source game-based driving simulation created to develop AV algorithms. Carla provides a flexible and well-documented API, pre-trained automated driving models, common roadmap standards (OpenDrive), and is actively maintained. While Carla provides possibilities for implementing, training, and evaluating fully automated driving algorithm, it does not explicitly support human-in-the-loop experiments, or make it easy to set up or create such experiments. Carla requires the user to create their own code to use human interfaces, which can be challenging and time-consuming depending on the experimenter's coding abilities.
To address these issues, we created JOAN, an open-source framework for conducting human-in-the-loop driving experiments. JOAN interfaces with Carla, is written in Python, and is fully customizable through code and graphical user interfaces (GUI). JOAN can be used with a variety of human input devices, including game console controllers (e.g.,Xbox or PlayStation), generic USB controllers (e.g., Logitech G920), or a SensoDrive high-fidelity steering wheel (for including haptic feedback). JOAN includes a framework for experiment design to create and execute experiments, and provides reliable data acquisition. These features can be accessed through a user-friendly interface, which means no extensive (knowledge of) programming nor a lot of time is required to set up new experiments.
JOAN mainly consists of modules, which communicate with each other through a News channel. The main modules are:
- CARLA interface
- Hardware Manager
- Data recorder
- Data Plotter
- Experiment Manager
- Haptic Controller Manager
See JOAN overview for more information, or read the module-specific documentation.
This documentation will help you in setting up CARLA and JOAN, guide you through your first steps, and provide more in depth information in how JOAN works and how you can tweak it to your preferences (which is highly encouraged!).
Have fun!
Documentation
For the most up-to-date documentation, visit https://joan.readthedocs.io/en/latest/.
Licensing and reference
JOAN is developed by a team (Joris, Olger, Andre, and Niek -> JOAN) of the Human-Robot Interaction group of Cognitive Robotics at Delft University of Technology.
Please see our license if you want to use our framework.
Please use the following citation:
Beckers, N., Siebinga, O., Giltay, J., & Van der Kraan, A. (2021). JOAN, a human-automated vehicle experiment framework. Retrieved from https://github.com/tud-hri/joan
Owner
- Name: Human-Robot Interaction Lab - Delft University of Technology
- Login: tud-hri
- Kind: organization
- Repositories: 4
- Profile: https://github.com/tud-hri
JOSS Publication
JOAN: a framework for human-automated vehicle interaction experiments in a virtual reality driving simulator
Authors
Human-Robot Interaction group, Department of Cognitive Robotics, Faculty 3mE, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands
Human-Robot Interaction group, Department of Cognitive Robotics, Faculty 3mE, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands
Human-Robot Interaction group, Department of Cognitive Robotics, Faculty 3mE, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands
Human-Robot Interaction group, Department of Cognitive Robotics, Faculty 3mE, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands
Tags
Human-automated vehicle interaction Automated driving Human factors experiments Simulation Virtual Reality CARLAGitHub Events
Total
- Watch event: 3
- Push event: 1
- Pull request event: 2
- Fork event: 3
Last Year
- Watch event: 3
- Push event: 1
- Pull request event: 2
- Fork event: 3
Committers
Last synced: 7 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Joris Giltay | j****y@t****l | 534 |
| niek | n****s@g****m | 428 |
| Joris Giltay | j****y@g****m | 173 |
| Olger Siebinga | o****a@t****l | 143 |
| André van der Kraan | a****n@t****l | 104 |
| Joris Giltay | j****y@1****0 | 39 |
| Olger Siebinga | o****a@f****m | 39 |
| Joris Giltay | j****y@M****l | 25 |
| Joris Giltay | j****y@1****0 | 11 |
| jnpgiltay | j****y@t****l | 4 |
| asevenster | a****r@s****l | 2 |
| imcatta | 4****a | 1 |
| azgonnikov@tudelft.nl | a****v@t****l | 1 |
| joan-dev | j****v@j****o | 1 |
| timomelman | t****n@t****l | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 26
- Total pull requests: 9
- Average time to close issues: 4 months
- Average time to close pull requests: 8 days
- Total issue authors: 12
- Total pull request authors: 5
- Average comments per issue: 2.58
- Average comments per pull request: 0.22
- Merged pull requests: 7
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 2
- Pull requests: 1
- Average time to close issues: 4 days
- Average time to close pull requests: 2 minutes
- Issue authors: 2
- Pull request authors: 1
- Average comments per issue: 2.0
- Average comments per pull request: 0.0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- lflipse (5)
- OlgerSiebinga (4)
- StijnOosterlinck (4)
- shamohammad (2)
- humanfactors (2)
- asevenster (2)
- KAArunmoli (1)
- FallenWyvern (1)
- loranjenkins (1)
- timeriverclef (1)
- niekbeckers (1)
- srpn97 (1)
Pull Request Authors
- lflipse (3)
- OlgerSiebinga (3)
- kseniia99kh (2)
- tomdries (1)
- imcatta (1)
Top Labels
Issue Labels
Pull Request Labels
Dependencies
- markdown-inline-graphviz-extension >=1.1
- mkdocs >=1.0
- pymdown-extensions *
- PyQt5 >=5.14.1
- PyQt5-sip >=12.7.1
- colour *
- flake8 *
- hidapi *
- keyboard *
- markdown_inline_graphviz_extension >=1.1
- mkdocs *
- numpy >=1.18.1
- pandas >=1.0.3
- pathlib *
- pymdown-extensions *
- pyqtgraph *
- wres *