Collaborative Research: CCRI: New: An Open Source Simulation Platform for AI Research on Autonomous Driving

Autonomous driving is transforming daily life and economy, with promised benefits like safe transportation and efficient mobility. Much of today’s research on autonomous driving experiments on expensive commercial vehicles. It is costly and risky to evaluate AI and machine learning methods on physical vehicles in real world.

Driving simulator provides a cost-effective and safe alternative for the development and evaluation of new AI algorithms. However, existing driving simulators with limited assets and complexity cannot accommodate the needs of the rapidly progressing AI fields. This project aims to develop an open-ended driving simulation platform that fosters innovations in various aspects of autonomous driving from perception to decision-making.

This platform will support realistic driving simulation with a diverse range of traffic assets and scenarios imported from real world. It will become a common experimental ground for researchers in academia and industry to develop new AI methods, share data and models, and benchmark the progress. The platform will grow into a community research infrastructure and have significant impacts on the blooming autonomous driving industry.

Additionally, it will provide interactive teaching toolkits for STEM education, particularly for students from underserved communities.

In this project, investigators will develop an open-source simulation platform called MetaDriverse for AI research on autonomous driving. This platform will serve as a research infrastructure and facilitate compelling research opportunities in various disciplines, including computer vision, computer graphics, machine learning, and human-machine interaction.

MetaDriverse will feature realistic visual appearance, interactive real-world scenarios and assets, and intuitive human control interface, allowing for the simulation of real-world driving experiences. It will also provide a wide range of tasks and benchmarks and the flexibility to design new ones, which will gauge the community’s collective effort and accelerate the research progress.

The key features and capabilities of the platform include (i) realistic visual perception and neural rendering, (ii) interactive simulation of real-world traffic scenarios, (iii) comprehensive benchmarks and model zoo, and (iv) community’s collective effort. This infrastructure will foster a wide range of opportunities and collaborations in the autonomous driving and intelligent transportation industries and bring significant societal and economic impacts.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.