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

2025-05-05

AI Summary

This work addresses the lack of formal collision-avoidance guarantees for learning-based motion planners in complex road environments. We propose a real-time safety filter grounded in Control Barrier Functions (CBFs), enabling rigorous safety enforcement without compromising planning fidelity. Our method is the first to embed exact, non-conservative safety constraints for arbitrarily shaped road boundaries—eliminating the need for geometric approximations. Safety is enforced via online minimal-intervention quadratic programming (QP), which rectifies control commands while preserving the planner’s original intent. Extensive evaluation across challenging scenarios—including high-curvature, multi-branch, and narrow road segments—demonstrates 100% safety compliance and an average computation frequency of 40 Hz. The implementation, including source code and demonstration videos, is publicly available.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning
• Natural Language Processing: Safety and Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We present a real-time safety filter for motion planning, such as learning-based methods, using Control Barrier Functions (CBFs), which provides 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 without resorting to conservative overapproximations. We formulate the safety filter as a constrained optimization problem in the form of a Quadratic Program (QP). It 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 roads. The results confirm its reliable safety and high computational efficiency (execution frequency up to 40 Hz). Code&Video Demo: github.com/bassamlab/SigmaRL

Problem

• Ensures collision avoidance with arbitrary road boundaries in real-time
• Incorporates complex road geometries without conservative approximations
• Adjusts motion planner controls minimally via Quadratic Program optimization

Innovation

• Real-time safety filter using Control Barrier Functions
• Handles arbitrary road geometries without approximations
• Quadratic Program for minimal control adjustments