As safety-critical autonomous vehicles (AVs) will soon become pervasive in our society, a number of safety concepts for trusted AV deployment have recently been proposed throughout industry and academia. Yet, achieving consensus on an appropriate safety concept is still an elusive task. In this paper, we advocate for the use of Hamilton-Jacobi (HJ) reachability as a unifying mathematical framework for comparing existing safety concepts, and through elements of this framework propose ways to tailor safety concepts (and thus expand their applicability) to scenarios with implicit expectations on agent behavior in a data-driven fashion. Specifically, we show that (i) existing predominant safety concepts can be embedded in the HJ reachability framework, thereby enabling a common language for comparing and contrasting modeling assumptions, and (ii) HJ reachability can serve as an inductive bias to effectively reason, in a learning context, about two critical, yet often overlooked aspects of safety: responsibility and context-dependency.
翻译:由于安全关键自主飞行器(AVs)将很快在我们的社会中变得普遍,最近在整个行业和学术界提出了一些可靠的AV部署安全概念。然而,就适当的安全概念达成共识仍是一项难以完成的任务。 在本文件中,我们主张将汉密尔顿-贾科比(HJ)可实现性作为比较现有安全概念的统一数学框架,并通过这一框架的要素提出如何使安全概念(从而扩大其适用性)适应以数据驱动的方式对代理行为产生隐含期望的情景。 具体而言,我们表明(一)现有主要安全概念可以嵌入HJ可实现性框架,从而能够形成一种用于比较和对比模型假设的共同语言,以及(二)HJ可实现性可以作为一种感性偏差,在学习中可以有效解释,即两个关键但经常被忽视的安全方面:责任和背景依赖性。