CARLA-Autoware-Bridge: Facilitating Autonomous Driving Research with a Unified Framework for Simulation and Module Development
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Abstract (arXiv)
Extensive testing is necessary to ensure the safety of autonomous driving modules. In addition to component tests, the safety assessment of individual modules also requires a holistic view at system level, which can be carried out efficiently with the help of simulation. Achieving seamless compatibility between a modular software stack and simulation is complex and poses a significant challenge for many researchers. To ensure testing at the system level with state-of-the-art AV software and simulation software, we have developed and analyzed a bridge connecting the CARLA simulator with the AV software Autoware Core/Universe. This publicly available bridge enables researchers to easily test their modules within the overall software. Our investigations show that an efficient and reliable communication system has been established. We provide the simulation bridge as open-source software at https://github.com/TUMFTM/Carla-Autoware-Bridge
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Feb 2024.
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Citing papers per year: 2025: 2; 2026: 3.
5 papers.
- 2026DMAVA: Distributed Multi-Autonomous Vehicle Architecture Using Autowarecited by 1
- 2025MultiDrive: A Co-Simulation Framework Bridging 2D and 3D Driving Simulation for AV Software Validationcited by 0
- 2025YOLO-APD: Enhancing YOLOv8 for Robust Pedestrian Detection on Complex Road Geometriescited by 0
- 2026MORPH-U: Multi-Objective Resilient Motion Planning for V2X-Enabled Autonomous Driving in High-Uncertainty Environments via Simulationcited by 0
- 2026Open-Source Autonomous Driving System Analysis and Multi-Disciplinary Hardware-in-the-Loop Research Paradigm with Reinforcement-Learning Testing and Large Language Modelscited by 0
- 2026Open-Source Autonomous Driving System Analysis and Multi-Disciplinary Hardware-in-the-Loop Research Paradigm with Reinforcement-Learning Testing and Large Language Models31 Aug 2026
- 2026MORPH-U: Multi-Objective Resilient Motion Planning for V2X-Enabled Autonomous Driving in High-Uncertainty Environments via Simulation8 May 2026
- 2026DMAVA: Distributed Multi-Autonomous Vehicle Architecture Using Autoware22 Jan 2026
- 2025YOLO-APD: Enhancing YOLOv8 for Robust Pedestrian Detection on Complex Road Geometries7 Jul 2025
- 2025MultiDrive: A Co-Simulation Framework Bridging 2D and 3D Driving Simulation for AV Software Validation20 May 2025