Neural Process-Based Reactive Controller for Autonomous Racing

• URL: http://arxiv.org/abs/2601.12143v1
• Date: Sat, 17 Jan 2026 19:16:46 GMT
• Title: Neural Process-Based Reactive Controller for Autonomous Racing
• Abstract summary: We introduce a novel reactive control framework for gap-based navigation using the Attentive Neural Process (AttNP) and a physics-informed extension, the PI-AttNP.<n>We derive and implement a control barrier function (CBF)-based filtering mechanism that analytically enforces collision avoidance constraints.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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