用实时安全滤波器确保复杂道路边界下的运动规划安全
A Real-Time Control Barrier Function-Based Safety Filter for Motion Planning with Arbitrary Road Boundary Constraints
- 基于控制屏障函数构建优化安全滤波器，直接处理任意多边形道路边界
- 在40赫兹频率下运行，仅做最小必要调整即保证避障安全
- 适合自动驾驶等对安全性与实时性要求高的场景
本文提出一种用于运动规划的实时安全滤波器，采用控制屏障函数（CBFs）为与道路边界的碰撞规避提供形式化保障。该方法可直接处理以折线表示的任意形状道路几何，无需保守的过近似。我们将安全滤波器建模为带约束的二次规划（QP）问题，通过最小必要调整原始规划器的控制指令来实现安全。在多种包含复杂道路边界的交通场景中进行了大量数值实验验证。结果表明，该方法具备可靠的安全部署能力和高计算效率，最高执行频率达40赫兹。代码及视频演示可在github.com/bassamlab/SigmaRL获取。
原文摘要 · Abstract (English)
We present a real-time safety filter for motion planning, including those that are learning-based, using Control Barrier Functions (CBFs) to provide formal guarantees for collision avoidance with road boundaries. A key feature of our approach is its ability to directly incorporate road geometries of arbitrary shape that are represented as polylines without resorting to conservative overapproximations. We formulate the safety filter as a constrained optimization problem as a Quadratic Program (QP), which achieves safety by making minimal, necessary adjustments to the control actions issued by the nominal motion planner. We validate our safety filter through extensive numerical experiments across a variety of traffic scenarios featuring complex road boundaries. The results confirm its reliable safety and high computational efficiency (execution frequency up to 40 Hz). Code reproducing our experimental results and a video demonstration are available at github.com/bassamlab/SigmaRL.
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