Attention-based neural architectures have become central to state-of-the-art methods in real-time nonlinear control. As these data-driven models continue to be integrated into increasingly safety-critical domains, ensuring statistically grounded and provably safe decision-making becomes essential. This paper introduces a novel reactive control framework for gap-based navigation using the Attentive Neural Process (AttNP) and a physics-informed extension, the PI-AttNP. Both models are evaluated in a simulated F1TENTH-style Ackermann steering racecar environment, chosen as a fast-paced proxy for safety-critical autonomous driving scenarios. The PI-AttNP augments the AttNP architecture with approximate model-based priors to inject physical inductive bias, enabling faster convergence and improved prediction accuracy suited for real-time control. To further ensure safety, we derive and implement a control barrier function (CBF)-based filtering mechanism that analytically enforces collision avoidance constraints. This CBF formulation is fully compatible with the learned AttNP controller and generalizes across a wide range of racing scenarios, providing a lightweight and certifiable safety layer. Our results demonstrate competitive closed-loop performance while ensuring real-time constraint satisfaction.

翻译：注意力驱动的神经架构已成为实时非线性控制领域前沿方法的核心。随着这些数据驱动模型日益融入安全关键领域，确保基于统计依据且可证明安全的决策制定变得至关重要。本文提出了一种新颖的基于间隙导航的反应式控制框架，采用注意力神经过程（AttNP）及其物理信息扩展版本（PI-AttNP）。两种模型均在模拟F1TENTH式阿克曼转向赛车环境中进行评估，该环境被选作安全关键自动驾驶场景的高速代理。PI-AttNP通过近似基于模型的先验知识增强AttNP架构，注入物理归纳偏置，从而实现更快的收敛速度并提升适用于实时控制的预测精度。为进一步保障安全性，我们推导并实现了基于控制屏障函数（CBF）的滤波机制，通过解析方法强制执行碰撞规避约束。该CBF公式与学习的AttNP控制器完全兼容，并能泛化至多种赛车场景，提供轻量级且可验证的安全层。实验结果表明，所提方法在确保实时约束满足的同时，实现了具有竞争力的闭环性能。