Introduction to CARLA Simulator
CARLA is an open-source platform designed for autonomous driving research. It has been meticulously developed to aid in the development, training, and validation of autonomous driving systems. Unlike many other simulators, CARLA not only offers open-source code but also includes a plethora of digital assets such as urban layouts, buildings, and vehicles. These assets are freely available and specifically created to facilitate research in autonomous driving.
Features of CARLA
CARLA stands out because it allows users to customize sensor suites and environmental conditions, providing flexibility and depth in simulation experiences. Researchers can manipulate these elements to create a highly realistic and relevant testing environment for autonomous vehicle systems.
For those interested in the specifics, CARLA is compatible with different versions of the Unreal Engine, specifically Unreal Engine 4.26 and Unreal Engine 5.3. It’s imperative for users to pick the right version as there are significant differences between them that could affect their research outcomes.
Getting Started with CARLA
CARLA can be downloaded on both Linux and Windows platforms. The developers recommend using a relatively robust computing system with specifications such as Intel i7 or i9 processors, or AMD Ryzen 7 or Ryzen 9 processors, at least 32 GB of RAM, and NVIDIA RTX 3070 or higher graphics cards to ensure smooth operation. Ubuntu 20.04 is the suggested operating system for Linux users.
Extensive Documentation
CARLA offers detailed online documentation that guides users through the installation process on both Linux and Windows. It also provides a comprehensive asset catalogue, a Python API reference, and a blueprint library to help users get the most out of the simulator.
The CARLA Ecosystem
The CARLA ecosystem is rich with various tools and extensions beneficial to autonomous driving research:
- CARLA Autonomous Driving Leaderboard: An automatic platform to validate autonomous driving systems.
- Scenario Runner: An engine used to execute diverse traffic scenarios within CARLA.
- ROS-bridge: Facilitates interfacing CARLA with the Robot Operating System (ROS).
- Driving Benchmarks: Toolkits for assessing autonomous driving performance.
- Conditional Imitation Learning: Tools to train and test these models in CARLA.
- AutoWare AV Stack: Bridges CARLA with the AutoWare autonomous vehicle stack.
- Reinforcement Learning: Offers code for running reinforcement learning models in CARLA.
- RoadRunner & Map Editor: Tools for creating and modifying road networks.
Community and Contributions
The CARLA team welcomes contributions from the community, offering guidelines to ensure collaborative and effective development. The community support is robust, with forums and discussion groups available on platforms like Discord and GitHub, allowing users to seek help and share insights.
Licensing and Legal Information
CARLA is distributed under the MIT License, with its assets available under the CC-BY License. This ensures that the simulator is free to use and modify, promoting openness in research. However, it’s important to note that CARLA’s dependencies like Unreal Engine and others might have different licensing terms, which users need to adhere to accordingly.
Overall, CARLA is an invaluable tool for researchers and developers in the field of autonomous driving, offering a comprehensive, flexible, and community-driven environment to test and refine driving systems efficiently and effectively.