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Traffic3D: A Rich 3D-Traffic Environment to Train Intelligent Agents

Traffic3D Front Page Image

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. 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.


Traffic3D is based on the Unity 3d games engine. The AI is written in Python3 with PyTorch.

Supported platforms

Traffic3D is tested on 64-bit Windows, Linux and OSX.

Download Traffic3D

Download the latest pre-built version of Traffic3D here:

To download the source code, please visit this page and click on the Download button (to the left of the Clone button).

You can see a list of Traffic3d releases on this page.


The current release of Traffic3D does not include autonomous vehicles, although users are encouraged to create their own autonomous vehicle scripts.

All vehicles currently move along pre-set paths.

Citing Traffic3D


The bibliography below contains a list of papers that describe the research on which Traffic3D is based. However, to cite the software itself, please see the details in the CITATION file in the main repository.

The Software Sustainability Institute has a guide on citing software, which may be helpful to some.

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.


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.