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The CARLA organization today named __NVIDIA__ as the latest sponsor of the CARLA Autonomous Driving Consortium. NVIDIA’s vice president of AI research, [Sanja Fidler](https://www.linkedin.com/in/sanja-fidler-2846a1a/?originalSubdomain=ca), will join the CARLA consortium board as NVIDIA’s representative, alongside existing representatives from Intel, Toyota Research Institute, Futurewei, and GuardStrike.
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NVIDIA’s participation in the consortium will provide many benefits to the CARLA community. Connecting the CARLA simulator to [__NVIDIA Omniverse__](https://www.nvidia.com/en-us/omniverse/) , a platform for integrating and building custom 3D pipelines, will provide rich new sources of content such as vehicles, pedestrians, and props for simulation in CARLA.
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Creating and accessing high-quality, diverse content for realistic, 3D virtual worlds used in autonomous driving simulation can be an arduous task. By joining forces and supporting a common specification for simulation-ready assets, artists and content providers can build assets that are ready to use without the usual days or weeks of work needed.
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Over the past six years, the CARLA team has made a huge effort to provide as many open and diverse 3D assets as possible, releasing 11 maps and thousands of 3D assets, including materials and texture maps. However, autonomous vehicles need training data to adapt to a highly disparate range of real-world environments. This motivates continuous growth of the scale and diversity of our content. The connection of CARLA to NVIDIA Omniverse will enable users to tap into a larger ecosystem of content and content-creation software, augmenting CARLA’s existing library.
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NVIDIA Omniverse is designed to facilitate the interchange of assets between content-creation tools. Omniverse is built on the open-source Universal Scene Description (USD) format. Incorporating support for USD will streamline the ingestion of newly created assets into CARLA, leveraging workflows that take advantage of the multitude of connected Omniverse applications, which includes industry-standard software such as Blender, Autodesk Maya and 3ds Max.
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Fidler, who is also an associate professor in the Department of Computer Science at the University of Toronto, is a strong advocate of CARLA and has cited the consortium’s work in several published papers and articles on AI and reinforcement learning. In her role on the CARLA board, she will share her expertise in AI and research as the consortium plans future developments.
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We are thrilled to collaborate with NVIDIA to bring a large collection of high-quality and diverse content for the benefit of the CARLA community. And this is just the beginning. Stay tuned for more updates!
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<li><strong>ROS integration</strong>: CARLA is provided with integration with <ahref="http://www.ros.org/">ROS</a> via our <ahref="https://github.com/carla-simulator/ros-bridge">ROS-bridge</a></li>
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<li><strong>Autonomous Driving baselines</strong>: we provide Autonomous Driving baselines as runnable agents in CARLA, including an <ahref="https://github.com/carla-simulator/carla-autoware">AutoWare</a> agent and a <ahref="https://github.com/felipecode/coiltraine">Conditional Imitation Learning</a> agent.</li>
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</ul>
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<h3>CARLA Talks</h3>
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<p>The team creates some additional content for users, besides the docs. This is a great way to cover different subjects such as detailed explanations for a specific module, latest improvements in a feature, future work and much more.</p>
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<h5>May 2020</h5>
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<ul><strong>General</strong>
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<li>Art improvements: environment and rendering — <ahref="https://youtu.be/ZZaHevsz8W8">video</a> | <ahref="https://drive.google.com/file/d/1l9Ztaq0Q8fNN5YPU4-5vL13eZUwsQl5P/view?usp=sharing">slides</a></li>
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<li>Core implementations: synchrony, snapshots and landmarks — <ahref="https://youtu.be/nyyTLmphqY4">video</a> | <ahref="https://drive.google.com/file/d/1yaOwf1419qWZqE1gTSrrknsWOhawEWh_/view?usp=sharing">slides</a></li>
<li>Pedestrians and their implementation — <ahref="https://youtu.be/Uoz2ihDwaWA">video</a> | <ahref="https://drive.google.com/file/d/1Tsosin7BLP1k558shtbzUdo2ZXVKy5CB/view?usp=sharing">slides</a></li>
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<li>Sensors in CARLA — <ahref="https://youtu.be/T8qCSet8WK0">video</a> | <ahref="https://drive.google.com/file/d/1UO8ZAIOp-1xaBzcFMfn_IoipycVkUo4q/view?usp=sharing">slides</a></li>
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</ul>
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<ul><strong>Modules</strong>
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<li>Improvements in the Traffic Manager — <ahref="https://youtu.be/n9cufaJ17eA">video</a> | <ahref="https://drive.google.com/file/d/1R9uNZ6pYHSZoEBxs2vYK7swiriKbbuxo/view?usp=sharing">slides</a></li>
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<li>Integration of autoware and ROS — <ahref="https://youtu.be/ChIgcC2scwU">video</a> | <ahref="https://drive.google.com/file/d/1uO6nBaFirrllb08OeqGAMVLApQ6EbgAt/view?usp=sharing">slides</a></li>
<li>OpenSCENARIO support — <ahref="https://drive.google.com/file/d/1g6ATxZRTWEdstiZwfBN1_T_x_WwZs0zE/view?usp=sharing">slides</a></li>
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<h3>Recommended reading</h3>
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<p>CARLA's functionality is covered extensively in the documentation. Here are some highlights covering some of CARLA's most useful and requested features.</p>
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<h5>Core features</h5>
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<ul>
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<li><strong><ahref="https://carla.readthedocs.io/en/latest/start_quickstart/">Quickstart</a></strong>: Getting started with CARLA is easy, this guide will show you how to install and run the simulator.</li>
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<li><strong><ahref="https://carla.readthedocs.io/en/latest/core_actors/">Actors</a></strong>: CARLA's actors are entities that interact within the simulation like vehicles, pedestrians and traffic signals. Get to know them here.</li>
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<li><strong><ahref="https://carla.readthedocs.io/en/latest/core_sensors/">Sensors</a></strong>: CARLA boasts an impressive array of models of real world sensors like cameras, LIDAR and RADAR. The simulator also gives access to privileged information such as ground truth semantic segmentation and depth information.</li>
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<li><strong><ahref="https://carla.readthedocs.io/en/latest/ts_traffic_simulation_overview/">Traffic Manager</a></strong>: CARLA's Traffic Manager controls NPCs to challenge your autonomous driving agent.</li>
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<li><strong><ahref="https://carla.readthedocs.io/projects/ros-bridge/en/latest/">ROS bridge</a></strong>: CARLA's ROS bridge enables seamless connection with the Robot Operating System.</li>
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</ul>
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<ul><strong>Features</strong>
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<li>Co-Simulations with SUMO and PTV-Vissim — <ahref="https://youtu.be/PuFSbj1PU94">video</a> | <ahref="https://drive.google.com/file/d/10DgMNUBqKqWBrdiwBiAIT4DdR9ObCquI/view?usp=sharing">slides</a></li>
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<li>Integration of RSS-lib — <ahref="https://drive.google.com/file/d/1whREmrCv67fOMipgCk6kkiW4VPODig0A/view?usp=sharing">slides</a></li>
<li><strong><ahref="https://carla.readthedocs.io/en/latest/tuto_first_steps/">First steps in CARLA</a></strong>: New to CARLA? Need some help with your first steps? <ahref="https://carla.readthedocs.io/en/latest/tuto_first/">This tutorial</a> will get you up and running.</li>
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<li><strong><ahref="https://carla.readthedocs.io/en/latest/tuto_G_bounding_boxes/">Bounding boxes</a></strong>: Need ground truth bounding boxes for vehicles or map features? <ahref="https://carla.readthedocs.io/en/latest/tuto_G_bounding_boxes/">This tutorial</a> shows you how to access them through CARLA's API.</li>
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<li><strong><ahref="https://carla.readthedocs.io/en/latest/tuto_content_authoring_maps/">Creating custom maps for CARLA</a></strong>: Create your own custom maps for CARLA, using CARLA's built in asset library or importing your own assets.</li>
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<li><strong><ahref="https://carla.readthedocs.io/en/latest/tuto_content_authoring_vehicles/">Creating custom vehicles for CARLA</a></strong>: Create your own custom vehicles for CARLA.</li>
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<li><strong><ahref="https://carla.readthedocs.io/en/latest/tuto_G_retrieve_data/">Recording your simulation</a></strong>: Your CARLA simulations can be recorded and replayed exactly, enabling you to repeat and compare results for different sensors or configurations.</li>
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