基于神经过程的反应式控制器用于自动驾驶赛车
Neural Process-Based Reactive Controller for Autonomous Racing
AI总结：
本文提出基于神经过程的反应式控制器，结合物理指导模型和控制屏障函数，实现自动驾驶赛车中的安全高效控制。
摘要：
基于注意力的神经架构已成为实时非线性控制最先进方法的核心。随着这些数据驱动模型继续被整合到日益安全关键的领域中，确保统计上站得住脚且可证明安全的决策变得至关重要。本文介绍了一种基于间隙导航的新型反应式控制框架，使用了注意神经过程（AttNP）和一个物理指导的扩展，即PI-AttNP。两种模型都在模拟的F1TENTH风格Ackermann转向赛车环境中进行评估，该环境被选为快速节奏的安全关键自动驾驶场景的代理。PI-AttNP通过近似模型驱动的先验来增强AttNP架构，注入物理归纳偏差，从而实现更快的收敛和更准确的预测，适用于实时控制。为进一步确保安全，我们推导并实现了基于控制屏障函数（CBF）的过滤机制，该机制通过分析强制避障约束。这种CBF公式完全兼容于学习的AttNP控制器，并能泛化到广泛的比赛场景中，提供了一层轻量且可证明安全的保护层。我们的结果表明，闭环性能具有竞争力，同时确保实时约束的满足。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
