论文标题
风险驱动的知觉系统设计
Risk-Driven Design of Perception Systems
论文作者
论文摘要
现代自主系统依靠感知模块将复杂的传感器测量处理成状态估计。然后，这些估计将传递给控制器，该控制器使用它们来做出关键的决策。因此，重要的是我们设计感知系统以最大程度地减少降低系统整体安全性的错误。我们开发了一种风险驱动的方法来设计感知系统，该方法解释了感知错误对完全集成，闭环系统性能的影响。我们制定了风险功能，以量化给定的感知错误对整体安全性的影响，并通过在损失功能中包含依赖风险的术语并在风险敏感区域中生成培训数据来展示如何使用它来设计更安全的感知系统。我们对基于逼真的飞机检测并避免应用的技术评估我们的技术，并表明风险驱动的设计在基线系统上降低了37％的碰撞风险。
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.
