ENVIA - Simulation environment for AI-driven vehicles

ENVIA - 人工智能驱动车辆的仿真环境

• 批准号: 576498-2022
• 负责人: Beltrame, GiovanniGM
• 金额: $ 8.88万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=754151
• 关键词: ENVIA; Simulation; environment; AI; driven

How do we learn the concepts of safety and how do we apply them to control algorithms for AI-driven vehicles? While deep reinforcement learning is a very common approach for modern artificial intelligence in terms of autonomous control (of robots, aircraft, factories, etc.), it carries by nature a fundamental safety risk that prevents its deployment in the context of many real industrial systems. Several researchers are trying to validate the safety of AI-driven systems. In this project, we use a different approach: we use classical control approaches to constrain AI-based systems and prevent actions that are not safe. We combine control theory and reinforcement learning to produce efficient and safe control algorithms, which can be initially trained in simulation, and then adapted and refined by meeting safety guarantees on the targeted real hardware. The principle is to use "barriers" to set strict safety limits for controllers trained by reinforcement learning (e.g., the controller of a drone). These barriers can themselves be learned automatically, and loosened or tightened as needed, trading off some level of risk to improve learning or performance. Imagine a person learning to ski with an instructor: they will take the risk of falling to learn, but this risk is limited and constrained by the instructor's knowledge and guidance. In a similar way, our barriers will prevent excessively risky actions taken by the AI. Our final goal is to design controllers that can be trained in simulation and then deployed on real hardware for adaptation and additional learning with tunable, calculated risk based on performance requirements. The economic and technical benefits for the industrial partners will be significant in the medium term, as the project will allow the application of deep reinforcement learning methods, which currently exceed the performance of all other known methods as well as that of human experts in more and more virtual environments where errors have no serious consequences, to real systems where safety is critical. Personnel working on the project will be highly sought after, and Canada will benefit in terms of scientific and industrialprestige, as well as by the economic impact of the new highly qualified personnel.

我们如何学习安全的概念，以及如何将它们应用于人工智能驾驶车辆的控制算法？虽然深度强化学习是现代人工智能在自主控制（机器人，飞机，工厂等）方面非常常见的方法，其本质上带有基本的安全风险，这阻止了其在许多真实的工业系统的环境中的部署。一些研究人员正试图验证人工智能驱动系统的安全性。在这个项目中，我们使用了一种不同的方法：我们使用经典的控制方法来约束基于AI的系统，并防止不安全的行为。我们将联合收割机控制理论和强化学习相结合，以产生高效安全的控制算法，这些算法可以在模拟中进行初始训练，然后通过满足目标真实的硬件上的安全保证进行调整和改进。其原理是使用“障碍”为经过强化学习训练的控制器设置严格的安全限制（例如，无人机的控制器）。这些障碍本身可以自动学习，并根据需要放松或收紧，以某种程度的风险来改善学习或表现。想象一下，一个人在教练的指导下学习滑雪：他们会冒着摔倒的风险去学习，但这种风险是有限的，并受到教练知识和指导的约束。以类似的方式，我们的屏障将防止人工智能采取过度冒险的行动。我们的最终目标是设计可以在仿真中训练的控制器，然后部署在真实的硬件上进行适应和额外的学习，并根据性能要求计算可调的风险。从中期来看，工业合作伙伴的经济和技术利益将是显著的，因为该项目将允许将深度强化学习方法应用于安全性至关重要的真实的系统，该方法目前超过了所有其他已知方法的性能，以及越来越多的虚拟环境中的人类专家的性能。从事该项目的人员将受到高度追捧，加拿大将在科学和工业声望方面受益，并将因新的高素质人员的经济影响而受益。