用多个备用策略并行评估，实时保障复杂环境下的系统安全
Policy Library CBF: Finite-Horizon Safety at Runtime via Parallel Rollouts
- 构建策略库并行滚动预测，动态选择最温和的安全应对方案
- 在4、8、12状态系统中实现更优安全覆盖，响应时间毫秒级
- 适合高动态复杂场景下的机器人与自动驾驶系统应用
在非结构化环境中，在线安全认证面临持续变化约束的挑战。本文提出策略库控制屏障函数（PL-CBF），一种运行时安全过滤器，通过并行有限时域滚动生成策略库中的备用策略，选择侵入性最小的安全模式，并通过求解二次规划最小修改默认策略以保证安全。基于闭环行为的有限时域语言度量，提供理论分析，刻画了策略库覆盖要求以认证有限时域安全性。在平面双积分器（4状态）、使用真实非线性车辆模型的高速路驾驶（8状态）以及拥挤动态环境中的3D四旋翼导航（12状态）上的仿真表明，相比单策略安全滤波器，本方法显著提升安全覆盖范围，同时保持毫秒级运行效率。
原文摘要 · Abstract (English)
Safety-critical autonomy in unstructured environments poses significant challenges for online safety certification under evolving constraints. We propose Policy Library Control Barrier Function~(PL-CBF), a runtime safety filter that evaluates a library of fallback policies via parallel finite-horizon rollouts, selects the least invasive safe mode, and enforces safety by solving a quadratic program that minimally modifies a nominal policy. We provide a theoretical analysis based on a finite-horizon language metric over closed-loop behaviors, characterizing policy-library coverage requirements for certifying finite-horizon safety. Simulations on a planar double-integrator (4 states), highway driving with abrupt friction changes using a realistic nonlinear vehicle model (8 states), and 3D quadrotor navigation in crowded dynamic environments (12 states) demonstrate improved safety coverage over single-policy safety filters while retaining millisecond-level runtime.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。