# Comparison between safety methods control barrier function vs. reachability analysis

arXiv: https://arxiv.org/abs/2106.13176 · HTML: https://mlsanity.com/paper/2106.13176/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2021.

## Built on by (most cited first, 6 of 6)

- [BarrierNet: A Safety-Guaranteed Layer for Neural Networks](https://mlsanity.com/paper/2111.11277/) (2021)
- [On the Forward Invariance of Neural ODEs](https://mlsanity.com/paper/2210.04763/) (2022)
- [Differentiable Control Barrier Functions for Vision-based End-to-End Autonomous Driving](https://mlsanity.com/paper/2203.02401/) (2022)
- [Optimal control barrier functions for RL based safe powertrain control](https://mlsanity.com/paper/2405.11391/) (2024)
- [Safe Reinforcement Learning for an Energy-Efficient Driver Assistance System](https://mlsanity.com/paper/2301.00904/) (2023)
- [Control invariant set enhanced safe reinforcement learning: improved sampling efficiency, guaranteed stability and robustness](https://mlsanity.com/paper/2305.15602/) (2023)

## Abstract (arXiv)

This report aims to compare two safety methods: control barrier function and Hamilton-Jacobi reachability analysis. We will consider the difference with a focus on the following aspects: generality of system dynamics, difficulty of construction and computation cost. A standard Dubins car model will be evaluated numerically to make the comparison more concrete.