Foundation of Safe Autonomous Systems

安全自治系统的基础

• 批准号: RGPIN-2017-06002
• 负责人: Lawford, Mark
• 金额: $ 1.68万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=656840
• 关键词: Foundation; Safe; Autonomous; Systems

Autonomous systems are about to make the jump to main stream commercial products that will be encounter by the public in their daily lives in the form of autonomous vehicles, delivery drones, etc. Semi autonomous vehicle features such as Tesla's autopilot and GM's Supercruise are already in widespread use. Elon Musk has gone so far as to claim that the Tesla ``Autopilot'' is safer than human drivers based upon the fact that over 100 million autopilot miles were driven before the first recorded fatality. While the safety of autonomous vehicles relative to human drivers is currently debatable, there is no doubt that such autonomous systems have the potential to deliver tremendous benefits. That said, they will only gain the public acceptance that would allow their widespread use if they are designed to be acceptably safe and reliable.******Using the first recorded North American fatality involving an autonomous driving system as a motivational example, the proposal outlines a program of research to provide the foundations to affordably design safe, reliable autonomous systems. Using the applicants previous experience in safety critical software systems and control of probabilistic discrete event systems, the proposal develops three main themes:******(1) Fault tolerant architectures that incorporate a standardized monitoring concept for subsystems that use machine learning (ML) and artificial intelligence (AI) techniques for environment sensing and control such as vision processing and path planning. Initially these subsystems will be treated as "black box'' systems and then eventually as "gray box" systems that can continuously have key aspects of their internal health and decisions monitored. ******(2) Methods and tools to provide a better observability of the current state of ML and AI systems in order to better understand when, how and why these systems fail. This improved ability to estimate the state or status of the ML and AI systems can then be used to provide fault diagnosis, improve reliability, and gain confidence in ML and AI decisions. ******(3) Standardized safety arguments in the form of assurance (safety) case templates for autonomous systems to allow these systems to be developed more reliably and affordably together with tool support to formally verify and analyze the dependability of the designs. ******The proposed research will provide results and HQP that will support Canadian companies in developing the coming wave of autonomous systems that will have to safely interact with the general public. With the global driver assist market alone estimated to be a $102 billion market by 2030, the work is of critical importance.

自动驾驶系统即将成为主流商业产品，这些产品将以自动驾驶汽车、无人机送货等形式出现在公众的日常生活中。特斯拉的自动驾驶仪和通用汽车的超级巡航等半自动驾驶汽车功能已经得到广泛使用。埃隆·马斯克（Elon Musk）甚至声称特斯拉的“自动驾驶仪”比人类驾驶员更安全，因为在第一次有记录的死亡事件发生之前，自动驾驶仪已经行驶了超过1亿英里。虽然自动驾驶汽车相对于人类驾驶员的安全性目前还存在争议，但毫无疑问，此类自动驾驶系统有潜力带来巨大的好处。也就是说，只有当它们被设计成可接受的安全和可靠时，它们才会获得公众的认可，从而允许它们的广泛使用。该提案以北美首次记录的涉及自动驾驶系统的死亡事件为例，概述了一项研究计划，为设计安全可靠的自动驾驶系统提供基础。利用申请人先前在安全关键软件系统和概率离散事件系统控制方面的经验，该提案开发了三个主要主题：**（1）容错架构，其结合了使用机器学习（ML）和人工智能（AI）技术进行环境感测和控制（如视觉处理和路径规划）的子系统的标准化监控概念。 最初，这些子系统将被视为“黑箱”系统，然后最终被视为“灰箱”系统，可以持续监控其内部健康和决策的关键方面。**（2）提供ML和AI系统当前状态的更好可观察性的方法和工具，以便更好地了解这些系统何时，如何以及为什么失败。这种改进的估计ML和AI系统的状态或状态的能力可以用于提供故障诊断，提高可靠性，并获得ML和AI决策的信心。******（3）以保证（安全）案例模板的形式为自主系统提供标准化的安全论证，以使这些系统的开发更加可靠和经济，并提供工具支持，以正式验证和分析设计的可靠性。** 拟议的研究将提供结果和HQP，以支持加拿大公司开发即将到来的自主系统浪潮，这些系统必须与公众安全互动。到2030年，仅全球驾驶员辅助市场估计就将达到1020亿美元，因此这项工作至关重要。