🎮 OpenADSim

Simulation environment for testing OpenADStack with CARLA or SUMO

OpenADSim is the official environment for closed-loop simulation of OpenADStack. It supports examplaric demonstrations, prototyping, and scenario-based testing with CARLA and SUMO.

🚀 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.

Watch the full teaser video.

🚀 Quick Start

Important

Check the general OpenADS system requirements and install Git LFS. Graphical applications require access to a local X11 server.

git lfs install
git clone --recursive https://github.com/openads-project/openadsim.git
cd openadsim

Start OpenADSim with the default configuration:

xhost +local:
docker compose up -d

Note

The initial image pull may take several minutes, depending on your system and internet connection.

Once the RViz and Manual Control windows open, you can plan a route with the Plan Route tool in RViz. Alternatively, drive manually with W/A/S/D and press B in Manual Control to hand control back to OpenADStack.

Important

Continue with the Getting Started guide. It covers the first simulation steps including usage and configuration.

📝 Documentation

The documentation covers:

• Getting Started

• Architecture

• Configuration

• Examples

  • Scenario Execution

  • Cooperative Perception

  • ML Planning

• Custom Integration

🧪 Examples

The links below provide detailed setup examples demonstrating the capabilities of OpenADSim and OpenADStack. Feel free to use these templates to customize your own simulation setups.

Example	Compose Profiles	Description
prototyping	carla, traffic, manual-testing, no-perception, planning (or sumo, no-perception, planning)	Default setup for experimenting with and manually testing OpenADStack. Traffic and testing profiles apply only to CARLA.
scenario-execution	carla, no-traffic, automated-testing, no-perception, planning	Sequential simulation of multiple scenarios in OpenSCENARIO format with automated evaluation.
cooperative-perception	carla, traffic, no-testing, perception, planning	Intersection scenario with multiple sensor-equipped participants sharing perception via V2X.
ml-planning	carla, no-traffic, manual-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.

OpenADSim combines the following elements:

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

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

• Supporting Services: communication infrastructure, visualization, and data recording

• Configuration and Data: maps, scenarios, vehicle and sensor configurations

• Deployment and Orchestration: Docker Compose configurable using environment variables and profiles, with a supporting Configuration GUI and predefined presets

Together, these elements form a modular simulation environment in which individual components can be configured and exchanged independently.

🙏 Acknowledgements

Citation

We hope that OpenADSim can help your research. If this is the case, please cite it using the metadata specified in CITATION.cff, or click on Cite this repository in GitHub’s About section on the top right.

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.}

Scalable Distributed Simulation-Based Testing for Automated Driving Systems, 2026

(arXiv, ResearchGate)

Christian Geller, Benedikt Haas, Lutz Eckstein Institute for Automotive Engineering (ika), RWTH Aachen University

^{Abstract – Virtual scenario-based testing is a key enabler for validating automated driving systems (ADS) and intelligent transport systems (ITS). However, executing large-scale test suites involving possibly thousands of scenarios remains labor-intensive and difficult to scale. This paper presents an end-to-end, DevOps-driven framework that automates build, deployment, and distributed execution of CARLA-based scenario tests of an ADS on a lightweight Kubernetes cluster. ROS 2 applications are packaged as standardized Kubernetes Helm charts generated from repository specifications, while entire simulation environments are composed declaratively via dynamic Helmfile manifests. The paper describes how a distributed testing workflow can be implemented in Argo Workflows to provision environments, aggregate and batch OpenSCENARIO test cases from configurable sources, execute scenarios in parallel across cluster nodes, and collect logs and resource metrics. In an evaluation on a multi-node K3s cluster running 200 scenarios, the best configuration speeds up end-to-end workflow time by more than a factor of eight compared to a sequential baseline. The results demonstrate significant gains in end-to-end execution time and quantify trade-offs between parallelism, orchestration overhead, and cluster stability. The framework is further demonstrated in a real-world ADS test application with connections to scenario sources and downstream evaluation modules. This demonstrates that the approach provides a strong foundation not only for scalable simulation testing, but also for generating traceable evidence that can support safety arguments.}

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
iEXODDUS	🇪🇺 European Union	101146091

_{^{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.}}