# A Real-Time Control Barrier Function-Based Safety Filter for Motion Planning with Arbitrary Road Boundary Constraints

arXiv: https://arxiv.org/abs/2505.02395 · HTML: https://mlsanity.com/paper/2505.02395/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2025.

## Built on by (most cited first, 3 of 3)

- [CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning](https://mlsanity.com/paper/2507.14903/) (2025)
- [DriveSafer: End-to-End Autonomous Driving with Safety Guidance](https://mlsanity.com/paper/2605.16737/) (2026)
- [Beyond Safety Filtering: Control Barrier Function-Informed Reinforcement Learning for Connected and Automated Vehicles](https://mlsanity.com/paper/2605.16894/) (2026)

## Abstract (arXiv)

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.