A safety-assured architecture for AI-enabled autonomous vehicles

支持人工智能的自动驾驶汽车的安全架构

• 批准号: RGPIN-2022-03944
• 负责人: Czarnecki, Krzysztof
• 金额: $ 4.01万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 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=751578
• 关键词: safety; assured; architecture; AI; enabled

Artificial intelligence (AI) has enabled new kinds of autonomous mobile robots that are on the verge of transforming multiple sectors of society. These include autonomous vehicles (AVs), sidewalk delivery robots, autonomous agricultural machines, cleaning robots, autonomous trains, and drones. Today, many of these robots fundamentally rely on deep learning for perception tasks such as object recognition, but deep learning starts to also penetrate prediction, planning, and control tasks. These advances greatly improve the adaptability of these systems and their ability to handle complex situations and interactions. These systems are expected to interact with humans and other robots in open environments that are subject to unexpected obstacles, varying weather conditions, and evolving infrastructures. Unfortunately, systems that rely on deep learning pose a great challenge to safety assurance, which is a major obstacle to their wide deployment. Deep neural networks (DNNs) lack human interpretability and may fail in unpredictable ways on new inputs. Further, the tasks they target, such as recognizing a pedestrian, evade complete specifications. These properties make the application of traditional safety assurance methods to these systems difficult or impossible. This Discovery grant will fund a cutting-edge research program to create the next generation of system architectures for AVs that take advantage of modern AI-technologies but are inherently assurable. The expected results include (1) an architecture for perception and planning that is inspired by the dual-process theory of human cognition and integrates the high-performance decision-making of DNNs with safety-relevant reasoning in interpretable, symbolic form; and (2) the methods to assure the safety of robots using such an architecture. The program will use the UW Moose, an AV developed by the PI's team, to demonstrate and evaluate the new technology on public roads. AVs and other mobile robots will have a major impact on our society and economy over the coming decades. It is expected that AVs can reduce accident rates by as much as 80%. In Canada, this would save 1,600 lives a year and over $55 billion in healthcare costs and lost productivity. The proposed research program targets current major roadblock to a wider adoption of such systems, which is assuring their safety. The program will generate cutting-edge research results in AI and robotics, share datasets and benchmarks to stimulate scientific and technological progress, create commercializable technology, and train 24 HQP in AI and robotics over the next five years. Hands-on experience on an AV will give these HQP a significant competitive advantage on the job market. The automotive sector is the single biggest contributor to Canada's manufacturing GDP. Canadian innovation and HQP training in AV technology will contribute to maintaining Canada's role as a world leader in the automotive sector and AI.

人工智能（AI）使新型的自动移动机器人濒临转变社会的多个部门。其中包括自动驾驶汽车（AV），人行道运输机器人，自动驾驶农业机器，清洁机器人，自动驾驶火车和无人机。如今，许多这些机器人从根本上依靠深度学习来进行感知任务，例如对象识别，但是深度学习也开始渗透预测，计划和控制任务。这些进步大大提高了这些系统的适应性及其处理复杂情况和互动的能力。预计这些系统将在开放环境中与人类和其他机器人相互作用，这些环境会遇到意外的障碍，变化的天气条件和不断发展的基础设施。不幸的是，依靠深度学习的系统对安全保证构成了巨大的挑战，这是他们广泛部署的主要障碍。深度神经网络（DNNS）缺乏人类的解释性，并且可能会以不可预测的方式失败。此外，他们针对的任务（例如识别行人）逃避了完整的规格。这些属性使传统的安全保证方法在这些系统中的应用很难或不可能。这项发现赠款将资助一项尖端的研究计划，以为利用现代AI技术的AVS创建下一代的系统体系结构，但本质上是可保证的。预期的结果包括（1）受感知和计划的架构，灵感来自人类认知的双过程理论，并将DNN的高性能决策与安全相关的推理整合为可解释的象征性形式； （2）使用这种架构确保机器人安全的方法。该计划将使用PI团队开发的AV UW Moose来展示和评估公共道路上的新技术。在未来几十年中，AV和其他移动机器人将对我们的社会和经济产生重大影响。预计AVS可以将事故发生率降低多达80％。在加拿大，这将每年节省1600人的生命，医疗保健成本超过550亿加元，生产力降低。拟议的研究计划以当前的主要障碍为目标，以更广泛地采用此类系统，以确保其安全性。该计划将在AI和机器人技术，共享数据集和基准测试中产生尖端的研究结果，以刺激科学和技术进步，创建商业化的技术，并在未来五年内用AI和机器人技术培训24 HQP。 AV上的动手经验将使这些HQP在就业市场上具有重要的竞争优势。汽车行业是加拿大制造GDP的最大贡献者。加拿大AV技术的创新和HQP培训将有助于维持加拿大在汽车领域和AI中的世界领导者的角色。