🎮 OpenADSim

Caution

🚧 This section is currently under construction. The OpenADSim repository will be made available within the next weeks.

Simulation environment for testing 🚗 OpenADStack with CARLA or SUMO.

This repository serves as a playground for a full closed-loop simulation of the OpenADStack within different simulation tools. It demonstrates various simulation capabilities and showcases their application in selected examples. The structure leverages Docker Compose to increase flexibility and usability.

🚀 Quick Start | 🧪 Examples | 🏗️ Architecture | 📝 Documentation | 🙏 Acknowledgements

Note

This repository is part of OpenADS, the Open Automated Driving Systems project. OpenADS and its modules have been initiated and are currently being maintained by the Institute for Automotive Engineering (ika) at RWTH Aachen University.

🚀 Quick Start

Make sure that the general OpenADS system requirements are met.

The complete simulation configuration is managed through a central .env file. Use the OpenADSim GUI to inspect the available options and configure the simulation:

docker compose up gui

Start the simulation:

docker compose up -d

Detailed tutorials and a description of all configuration options are part of the 📝 OpenADS documentation.

📝 Documentation

The documentation covers:

• Getting Started

• Architecture

• Configuration

• Examples

  • Scenario Testing

  • Cooperative Perception

  • ML Planning

• Custom Integration

🧪 Examples

Each link below leads to a detailed description of an example that can be used as a template and adapted to your needs.

Example	Compose Profiles	Description
prototyping	carla/sumo, random-testing, no-perception, planning	Default setup for experimenting with and manually testing OpenADStack.
scenario-testing	carla, automated-testing, no-perception, planning	Sequential simulation of multiple scenarios in OpenSCENARIO format with automated evaluation.
cooperative-perception	carla, manual-testing, perception, planning	Intersection scenario with multiple sensor-equipped participants sharing perception via V2X.
ml-planning	carla, no-testing, no-perception, ml-planning	Partial offloading of planning tasks to a machine learning-based planning module with adaptive orchestration.
…	…	…

🏗️ Architecture

The detailed architecture and service structure are described in Architecture and Services.

In general, OpenADSim is organized into five layers:

• Simulation Layer: simulator core, scenario execution, environment control, and simulator-specific ROS integration

• OpenADStack Layer: localization, perception, understanding, planning, and control services

• Tooling Layer: service communication, visualization, and bag recording

• Storage Layer: maps, scenarios, parameters, and recorded data

• Orchestration Layer: hierarchical Docker Compose setup using environment variables, profiles, examples, and a simplified GUI configuration

This layered design supports modular integration and allows individual components to be exchanged while preserving the overall system architecture.

🙏 Acknowledgements

Citation

We hope that our simulation framework OpenADSim supports your research and development. If it does, please cite it using the following metadata.

@misc{OpenADSim,
  author = {Geller, Christian and Damaschke, Silas and Busch, Jean-Pierre and van Kempen, Raphael and Linden, Guido},
  title = {{OpenADSim}},
  url = {https://github.com/openads-project/openadsim},
  year = {2026}
}

Related Publications

CARLOS: An Open, Modular, and Scalable Simulation Framework for the Development and Testing of Software for C-ITS, 2024

(IEEEXplore, arXiv, ResearchGate)

Christian Geller, Benedikt Haas, Amarin Kloeker, Jona Hermens, Bastian Lampe, Till Beemelmanns, Lutz Eckstein Institute for Automotive Engineering (ika), RWTH Aachen University

^{Abstract – Future mobility systems and their components are increasingly defined by their software. The complexity of these cooperative intelligent transport systems (C-ITS) and the ever-changing requirements posed at the software require continual software updates. The dynamic nature of the system and the practically innumerable scenarios in which different software components work together necessitate efficient and automated development and testing procedures that use simulations as one core methodology. The availability of such simulation architectures is a common interest among many stakeholders, especially in the field of automated driving. That is why we propose CARLOS - an open, modular, and scalable simulation framework for the development and testing of software in C-ITS that leverages the rich CARLA and ROS ecosystems. We provide core building blocks for this framework and explain how it can be used and extended by the community. Its architecture builds upon modern microservice and DevOps principles such as containerization and continuous integration. In our paper, we motivate the architecture by describing important design principles and showcasing three major use cases - software prototyping, data-driven development, and automated testing. We make CARLOS and example implementations of the three use cases publicly available at ika-rwth-aachen/carlos.}

Licensing

The source code in this repository is licensed under Apache-2.0, see LICENSE. Container images provided by this repository may contain third-party software shipped with their own license terms.

Funding

Development and maintenance of this repository are supported by the following projects. We acknowledge the funding of the respective institutions.

Project	Funding Institution	Grant Number
AIGGREGATE	🇪🇺 European Union	101202457
AIthena	🇪🇺 European Union	101076754
autotech.agil	🇩🇪 Federal Ministry for Research, Technology and Space (BMFTR)	01IS22088A

_{^{Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Climate, Infrastructure and Environment Executive Agency (CINEA). Neither the European Union nor CINEA can be held responsible for them.}}