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High-Order Control Barrier Functions: Insights and a Truncated Taylor-Based Formulation

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arxiv 2503.15014 v1 pith:GK56ZL4V submitted 2025-03-19 eess.SY cs.ROcs.SY

classification eess.SYcs.ROcs.SY
keywords approachtruncatedbarrierconditiondesignhocbfcomplexitycontrol
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We examine the complexity of the standard High-Order Control Barrier Function (HOCBF) approach and propose a truncated Taylor-based approach that reduces design parameters. First, we derive the explicit inequality condition for the HOCBF approach and show that the corresponding equality condition sets a lower bound on the barrier function value that regulates its decay rate. Next, we present our Truncated Taylor CBF (TTCBF), which uses a truncated Taylor series to approximate the discrete-time CBF condition. While the standard HOCBF approach requires multiple class K functions, leading to more design parameters as the constraint's relative degree increases, our TTCBF approach requires only one. We support our theoretical findings in numerical collision-avoidance experiments and show that our approach ensures safety while reducing design complexity.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Uncertainty-Aware Safety-Critical Decision and Control for Autonomous Vehicles at Unsignalized Intersections

    cs.RO 2025-05 conditional novelty 5.0 of 10

    USDC combines ensemble distributional RL with a high-order control barrier function and uncertainty-based switching to reduce collisions at unsignalized intersections while preserving traffic efficiency in simulation.

  2. CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning

    cs.RO 2025-07 conditional novelty 4.0 of 10

    CoMoCAVs proposes a Mixture of Experts inspired hierarchical RL framework that couples lane-selection decisions with lane-specific motion-planning policies for autonomous highway driving.

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