Modern autonomous systems rely on perception modules to process complex sensor measurements into state estimates. These estimates are then passed to a controller, which uses them to make safety-critical decisions. It is therefore important that we design perception systems to minimize errors that reduce the overall safety of the system. We develop a risk-driven approach to designing perception systems that accounts for the effect of perceptual errors on the performance of the fully-integrated, closed-loop system. We formulate a risk function to quantify the effect of a given perceptual error on overall safety, and show how we can use it to design safer perception systems by including a risk-dependent term in the loss function and generating training data in risk-sensitive regions. We evaluate our techniques on a realistic vision-based aircraft detect and avoid application and show that risk-driven design reduces collision risk by 37% over a baseline system.
翻译:现代自主系统依靠感知模块将复杂的感官测量结果纳入国家估计。然后将这些估算结果传递给一名控制员,由他来作出安全方面的关键性决定。因此,我们必须设计感知系统,以尽量减少降低系统总体安全的错误;我们开发一种风险驱动方法,设计感知系统,以说明感知错误对全集成、闭环系统性能的影响。我们制定了一种风险功能,以量化特定感知错误对总体安全的影响,并表明我们如何能够利用它设计更安全的认识系统,在损失功能中加入一个依赖风险的术语,并在风险敏感区域生成培训数据。我们评估我们关于现实的、基于愿景的飞机探测和避免应用的技术,并表明风险驱动设计比基线系统减少37%的碰撞风险。