traffic3d
Unattended mirror of https://gitlab.com/traffic3d/traffic3d -- Please use the GitLab repo to raise issues or PRs.
Science Score: 23.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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○codemeta.json file
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○.zenodo.json file
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✓DOI references
Found 3 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (12.3%) to scientific vocabulary
Keywords
Repository
Unattended mirror of https://gitlab.com/traffic3d/traffic3d -- Please use the GitLab repo to raise issues or PRs.
Basic Info
- Host: GitHub
- Owner: traffic3d
- License: mpl-2.0
- Language: C#
- Default Branch: develop
- Homepage: https://traffic3d.org/
- Size: 1.1 GB
Statistics
- Stars: 4
- Watchers: 1
- Forks: 1
- Open Issues: 2
- Releases: 0
Topics
Metadata Files
README.md
Traffic 3D
Traffic3D is a new traffic simulation paradigm, built to push forward research in human-like learning (for example, based on photo-realistic visual input). It provides a fast, cheap and scalable proxy for real-world traffic environments, based on the Unity 3d platform. This implies effective simulation of diverse and dynamic 3D-road traffic scenarios, closely mimicking real-world traffic characteristics such as faithful simulation of individual vehicle behaviour, their precise physics of movement and photo-realism. Traffic3D can facilitate research across multiple domains, including reinforcement learning, object detection and segmentation, unsupervised representation learning and visual question answering.
For more details please see https://traffic3d.org.
[[TOC]]
CLI options
Traffic3D has custom command line options using following flag:
-executeMethod CustomCommandLineArguments.Run
The custom options are:
-JSONConfigFile "filename.json"- Import settings from a JSON file (see below for config details)-OpenScene "Scenes/sceneName.unity"- Open a scene-RunBenchmark true|false- Run Benchmark-RandomSeed INT- Set an integer seed for the Unity random number generator
Examples
```sh
Using all custom options.
${UNITY_EXECUTABLE:-xvfb-run --auto-servernum --server-args='-screen 0 640x480x24' /opt/Unity/Editor/Unity} \ -projectPath $(pwd)/Traffic3D \ -testPlatform playmode \ -testResults $(pwd)/playmode-results.xml \ -executeMethod CustomCommandLineArguments.Run \ -JSONConfigFile "config.json" \ -OpenScene "Scenes/NightDemo.unity" \ -RandomSeed 12345 \ -RunBenchmark true \ -logFile \ -batchmode ```
If you are using a Unity cache server you can also add a line, such as: -CacheServerIPAddress XXX.XXX.XXX.XXX:8126.
Evacu-agent
Evacu-agent is an evacuation simulation that extends the pedestrian behaviour in Traffic3D.
Camera Controls
Panning:
- Pan up:
up arrow,w. - Pan left:
left arrow,a. - Pan down:
down arrow,s. - Pan right:
right arrow,d. - Increase pan speed while pressed:
left shift.
Rotating:
- Rotate left:
q. - Rotate right:
e.
Zooming:
- Zoom out:
z. - Zoom in:
c.
Configuration
Configuration is used to bulk import initialisation values into the Traffic3D system.
Normally the values are direct mappings of the values that can be changed within the Unity UI.
For example the vehicleFactoryConfig object within the config example below allows us to pass data directly to the specified fields.
e.g. The highRangeRespawnTime field in the config maps to the highRangeRespawnTime field in the game object.
Examples
See test_config.json for an example of how to structure the file.
Bugs and feature requests
Please report issues via the issue tracker.
Citing the Traffi3D software
Please see the CITATION file.
Papers that use Traffic3D
Garg, D., Chli, M. and Vogiatzis, G., 2019. A Deep Reinforcement Learning Agent for Traffic Intersection Control Optimization. In Proceedings of the IEEE Intelligent Transportation Systems Conference (ITSC) IEEE.
Garg, D., Chli, M. and Vogiatzis, G., 2018, September. Deep reinforcement learning for autonomous traffic light control. In 2018 3rd IEEE International Conference on Intelligent Transportation Engineering (ICITE) (pp. 214-218). IEEE.
Bibliography
Garg, D., Chli, M. and Vogiatzis, G., 2019, June. Traffic3D: A Rich 3D-Traffic Environment to Train Intelligent Agents. In International Conference on Computational Science (pp. 749-755). Springer, Cham.
Garg, D., Chli, M. and Vogiatzis, G., 2019, May. Traffic3D: A New Traffic Simulation Paradigm. In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems (pp. 2354-2356). International Foundation for Autonomous Agents and Multiagent Systems.
Developers
Current developers:
- Callum Bugajski - @callumbugajski via Beautiful Canoe
Previous developers:
- Hishaam Iqbal - @iqbalh7
- Josh Leeder - @leeder.josh via Beautiful Canoe
Based on original code by Deepeka Garg - @gargd03.
Credits
- LatLonConverter Class - Licensed under Creative Commons Attribution-ShareAlike 2.0 (CC BY-SA) - developed by Florian Mller
- The NUnit to JUnit XSLT translator is from the project nunit/nunit-transforms, published under the MIT license by Paul Hicks.
- GetLineIntersection Method in VehiclePath - Licensed under MIT
License
This software is licensed under the Mozilla Public License Version 2.0. Copies of the license can also be obtained directly from Mozilla.
Getting started with development
Please read CONTRIBUTING.md before you start working on this repository.