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

自动驾驶正在改变日常生活和经济，承诺的好处包括安全的交通和高效的机动性。今天关于自动驾驶的大部分研究都是在昂贵的商用车上进行的。在现实世界中评估人工智能和机器学习方法在真实世界中的物理车辆上是昂贵和危险的。驾驶模拟器为开发和评估新的人工智能算法提供了一种经济有效和安全的替代方案。然而，现有的驾驶模拟器资产和复杂性有限，不能适应快速发展的人工智能领域的需求。该项目旨在开发一个开放式驾驶模拟平台，促进自动驾驶从感知到决策的各个方面的创新。该平台将使用从真实世界导入的各种交通资产和场景来支持逼真的驾驶模拟。它将成为学术界和工业界研究人员开发新的人工智能方法、共享数据和模型、并对进展进行基准的共同试验场。该平台将成长为一个社区研究基础设施，并对蓬勃发展的自动驾驶行业产生重大影响。此外，它还将为STEM教育提供互动教学工具包，特别是针对服务不足社区的学生。在这个项目中，研究人员将开发一个名为MetaDriverse的开源模拟平台，用于自动驾驶的人工智能研究。该平台将作为研究基础设施，促进计算机视觉、计算机图形学、机器学习和人机交互等不同学科的引人注目的研究机会。MetaDriverse将具有逼真的视觉外观、互动的真实世界场景和资产以及直观的人类控制界面，允许模拟真实世界的驾驶体验。它还将提供广泛的任务和基准，并灵活地设计新的任务和基准，以衡量社区的集体努力和加快研究进展。该平台的主要特点和能力包括(I)逼真的视觉感知和神经渲染，(Ii)真实世界交通场景的交互模拟，(Iii)全面的基准和模型动物园，以及(Iv)社区的集体努力。这一基础设施将在自动驾驶和智能交通行业促进广泛的机会和合作，并带来重大的社会和经济影响。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.