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.

翻译：我们提出了一种用于运动规划的实时安全滤波器（含基于学习的方法），该滤波器利用控制障碍函数（CBFs）为道路边界避碰提供形式化保障。本方法的核心优势在于能够直接处理以多段线表示的任意形状道路几何结构，无需采用保守的过度近似。我们将安全滤波器构建为二次规划（QP）形式的约束优化问题，通过最小程度地必要调整名义运动规划器发出的控制指令来实现安全保障。通过涵盖多种复杂道路边界交通场景的大量数值实验验证，本安全滤波器在可靠安全性（执行频率达40 Hz）与高计算效率方面均表现出色。复现实验结果的代码及演示视频详见github.com/bassamlab/SigmaRL。