Control Barrier Functions and Input-to-State Safety with Application to Automated Vehicles
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Abstract (arXiv)
Balancing safety and performance is one of the predominant challenges in modern control system design. Moreover, it is crucial to robustly ensure safety without inducing unnecessary conservativeness that degrades performance. In this work we present a constructive approach for safety-critical control synthesis via Control Barrier Functions (CBF). By filtering a hand-designed controller via a CBF, we are able to attain performant behavior while providing rigorous guarantees of safety. In the face of disturbances, robust safety and performance are simultaneously achieved through the notion of Input-to-State Safety (ISSf). We take a tutorial approach by developing the CBF-design methodology in parallel with an inverted pendulum example, making the challenges and sensitivities in the design process concrete. To establish the capability of the proposed approach, we consider the practical setting of safety-critical design via CBFs for a connected automated vehicle (CAV) in the form of a class-8 truck without a trailer. Through experimentation we see the impact of unmodeled disturbances in the truck's actuation system on the safety guarantees provided by CBFs. We characterize these disturbances and using ISSf, produce a robust controller that achieves safety without conceding performance. We evaluate our design both in simulation, and for the first time on an automotive system, experimentally.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Jun 2022.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 4; 2025: 3; 2026: 2.
11 papers.
- 2024CBF-LLM: Safe Control for LLM Alignmentcited by 8
- 2025Designing Latent Safety Filters using Pre-Trained Vision Modelscited by 3
- 2022Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functionscited by 2
- 2023Exact Recovery for System Identification with More Corrupt Data than Clean Datacited by 2
- 2026Safe Control using Learned Safety Filters and Adaptive Conformal Inferencecited by 2
- 2024Enhanced Safety in Autonomous Driving: Integrating Latent State Diffusion Model for End-to-End Navigationcited by 1
- 2024Pre-Trained Vision Models as Perception Backbones for Safety Filters in Autonomous Drivingcited by 1
- 2024Pareto Control Barrier Function for Inner Safe Set Maximization Under Input Constraintscited by 0
- 2025Control Barrier Function for Aligning Large Language Modelscited by 0
- 2025Probabilistic Safety Guarantee for Stochastic Control Systems Using Average Reward MDPscited by 0
- 2026VLM-CASE: Vision-Language Model Enabled Context-Adaptive Safety Envelopes for Anticipatory Safe Autonomous Drivingcited by 0
- 2026VLM-CASE: Vision-Language Model Enabled Context-Adaptive Safety Envelopes for Anticipatory Safe Autonomous Driving6 Jul 2026
- 2026Safe Control using Learned Safety Filters and Adaptive Conformal Inference20 Apr 2026
- 2025Probabilistic Safety Guarantee for Stochastic Control Systems Using Average Reward MDPs11 Nov 2025
- 2025Control Barrier Function for Aligning Large Language Models5 Nov 2025
- 2025Designing Latent Safety Filters using Pre-Trained Vision Models18 Sep 2025
- 2024Pre-Trained Vision Models as Perception Backbones for Safety Filters in Autonomous Driving29 Oct 2024
- 2024Pareto Control Barrier Function for Inner Safe Set Maximization Under Input Constraints5 Oct 2024
- 2024CBF-LLM: Safe Control for LLM Alignment28 Aug 2024
- 2024Enhanced Safety in Autonomous Driving: Integrating Latent State Diffusion Model for End-to-End Navigation8 Jul 2024
- 2023Exact Recovery for System Identification with More Corrupt Data than Clean Data17 May 2023
- 2022Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions23 Aug 2022