Correctness Guarantees for the Composition of Lane Keeping and Adaptive Cruise Control
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Abstract (arXiv)
This paper develops a control approach with correctness guarantees for the simultaneous operation of lane keeping and adaptive cruise control. The safety specifications for these driver assistance modules are expressed in terms of set invariance. Control barrier functions are used to design a family of control solutions that guarantee the forward invariance of a set, which implies satisfaction of the safety specifications. The control barrier functions are synthesized through a combination of sum-of-squares program and physics-based modeling and optimization. A real-time quadratic program is posed to combine the control barrier functions with the performance-based controllers, which can be either expressed as control Lyapunov function conditions or as black-box legacy controllers. In both cases, the resulting feedback control guarantees the safety of the composed driver assistance modules in a formally correct manner. Importantly, the quadratic program admits a closed-form solution that can be easily implemented. The effectiveness of the control approach is demonstrated by simulations in the industry-standard vehicle simulator Carsim.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Sep 2016.
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Citing papers per year: 2021: 3; 2022: 0; 2023: 2; 2024: 2.
7 papers.
- 2023The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systemscited by 77
- 2021Learning Safe Multi-Agent Control with Decentralized Neural Barrier Certificatescited by 71
- 2021Learning Robust Output Control Barrier Functions from Safe Expert Demonstrationscited by 19
- 2021Learning Robust Hybrid Control Barrier Functions for Uncertain Systemscited by 14
- 2023Exact Verification of ReLU Neural Control Barrier Functionscited by 14
- 2024Gameplay Filters: Robust Zero-Shot Safety through Adversarial Imaginationcited by 12
- 2024Verification of Neural Control Barrier Functions with Symbolic Derivative Bounds Propagationcited by 12
- 2024Verification of Neural Control Barrier Functions with Symbolic Derivative Bounds Propagation4 Oct 2024
- 2024Gameplay Filters: Robust Zero-Shot Safety through Adversarial Imagination1 May 2024
- 2023Exact Verification of ReLU Neural Control Barrier Functions13 Oct 2023
- 2023The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systems11 Sep 2023
- 2021Learning Robust Output Control Barrier Functions from Safe Expert Demonstrations18 Nov 2021
- 2021Learning Robust Hybrid Control Barrier Functions for Uncertain Systems16 Jan 2021
- 2021Learning Safe Multi-Agent Control with Decentralized Neural Barrier Certificates14 Jan 2021