switching-region design

This work addresses the challenge of ensuring safe and autonomous robot navigation in complex dynamic environments by proposing a novel “Control Barrier Corridor” framework. It unifies control barrier functions with safety corridors for the first time, reformulating safety constraints as locally feasible target regions. By integrating feedback control with convex optimization, the method generates reference trajectories that guarantee continuous safety in real time. The approach is validated on fully actuated systems, unicycle models, and linear output regulation systems, demonstrating its broad applicability. A key contribution lies in establishing a tunable trade-off between safety and responsiveness, enabling verifiably safe, persistent, and adaptive exploration even in unknown environments.

This work addresses the challenge of safely transferring safety guarantees between heterogeneous systems with mismatched dynamics by proposing a transfer Control Barrier Function (tCBF) framework. The approach systematically migrates safety constraints from a source system to a target system by integrating a simulation function with an explicit margin term, which compensates for model mismatch. Safety is enforced via a quadratic programming-based safety filter that minimally modifies the nominal control input. Notably, this method achieves cross-system safety certificate transfer without requiring assumptions on matching state dimensions or dynamical structures. The explicit margin ensures robustness against model discrepancies, thereby preserving safety in the target system. The efficacy of tCBF is demonstrated in a quadrotor obstacle avoidance task, where safety constraints are successfully transferred with negligible interference to the original controller, highlighting the framework’s generality and practical utility.

This work addresses safety concerns in packet-switched networks—such as queue instability, tail latency spikes, and resource starvation—arising from adaptive or learning-driven control policies. To mitigate these issues, the paper proposes a composable action authentication framework that inserts an authentication operator between the proposer and the data plane. This operator enforces predefined safety constraints on closed-loop control through either certificate-based action projection or a relaxed fallback mechanism. The approach unifies multiple objectives—including backlog upper bounds, service lower bounds, and drift constraints—by integrating Foster–Lyapunov drift analysis, small-gain cyclic closure theory, and robust calibration under delayed telemetry. Experimental results demonstrate that, even under delayed observations, weak proposers, or overload conditions, the system maintains stability and safety in a byte-level closed-loop backend while generating auditable traffic envelopes to support downstream compositional verification.

This work addresses the foundational graph-structural problem of path-completeness for barrier functions in safety verification of switched systems, establishing that path-completeness is a necessary condition for deriving sound and complete safety certificates—a necessity rigorously proven for the first time. Method: We develop a simulation-relation-based theory for quantifying the conservativeness of path-complete graphs, yielding a dynamics- and template-agnostic criterion for comparing safety guarantees across distinct graph structures. Our approach integrates combinatorial graph theory, barrier function theory, and an algebraic-combinatorial verification framework. Contribution/Results: The framework enables quantitative assessment of safety-determination capability at the graph-structural level, providing both theoretical foundations and a systematic methodology for selecting optimal path-complete graph structures in safety verification.

This work addresses the challenge of real-time safety-critical control for sampled-data systems subject to high relative-degree safety constraints and state-input coupling. We propose Zero-Order Control Barrier Functions (ZOCBFs), which eliminate reliance on Lie derivatives or higher-order time derivatives; instead, safety conditions are formulated solely via finite differences of barrier function values between consecutive sampling instants—achieving the first zero-order discrete-time CBF formulation. The method unifies treatment of coupled state and input constraints, accommodates arbitrary relative-degree safety requirements, and provides three efficient implementation strategies: quadratic programming, numerical root-finding, and lookup-table-based evaluation. Evaluated on collision avoidance and rollover prevention over uneven terrain, ZOCBFs guarantee rigorous safety enforcement while significantly reducing computational overhead, enabling real-time, scalable safety-critical control.

论文提出一种权威分解框架，通过行动相关方法确定哪些信任域联合体能导致受保护执行，解决高风险自动化系统中控制分布问题。

论文解决了四旋翼无人机在机体速率限制下的安全问题，通过定义状态和先前输入的增强对来创建逃逸屏障函数，提高了安全性。

This study addresses the absence of auditable, multi-signal governance standards for dynamically switching AI autonomy in human-AI collaboration. To tackle this challenge, the work proposes a handover problem framework that constructs a Handover Readiness Score (HRS) by fusing multidimensional signals. By integrating hysteresis-based transition strategies with hard safety guardrails, the framework achieves reversible, recoverable, and auditable control over autonomy allocation. The primary contribution lies in establishing the first foundation for autonomy governance based on explicit composite criteria to mitigate complementary failure modes. Validated through applications in software engineering and manufacturing domains, the proposed approach effectively resolves the limitation of single-metric systems in detecting the gradual erosion of operational readiness.

This study addresses the lack of explicit behavioral specifications and validation criteria for autonomous driving systems within their Operational Design Domain (ODD). Building upon the PEGASUS six-layer model, the authors propose a comprehensive behavioral capability taxonomy encompassing 21 capabilities across three key scenarios—highway, urban, and interchange environments—structured along longitudinal and lateral control dimensions and characterized by four attributes: safety, compliance, comfort, and efficiency. The work innovatively establishes a cross-mapping between parameterized ODD definitions and behavioral specifications, thereby introducing, for the first time, a verifiable and testable behavioral specification layer. Notably, interchange scenarios are identified as a structurally distinct and underexplored domain. Leveraging a rule-driven trajectory optimization system and aligned with standards such as SAE J3016, the proposed framework enables standardized, actionable behavioral capability assessment, supports SOTIF-compliant evidence generation, and demonstrates practical efficacy as an operational specification layer in real-world deployments.

This study addresses the limitation of conventional safety certificates requiring re-synthesis under varying operating conditions by proposing a conditional Hamilton-Jacobi reachability framework that enables adaptive, universal safety certificates across diverse scenarios. Methodologically, leveraging an eight-state vehicle dynamics model and boundary observation encoding, this work constructs the first context-conditioned single-certificate mechanism. Through conditional value function learning and discrete-time control barrier function filtering, the certificate dynamically generalizes across friction, geometry, and disturbances without re-synthesis, adapting seamlessly to unseen deployment environments. Real-world vehicle experiments under extreme handling maneuvers demonstrate that the proposed approach maintains zero lateral constraint violations with only 3.1% performance degradation, validating its highly efficient transferability.