The growing need for high-performance controllers in safety-critical applications like autonomous driving has been motivating the development of formal safety verification techniques. In this paper, we design and implement a predictive safety filter that is able to maintain vehicle safety with respect to track boundaries when paired alongside any potentially unsafe control signal, such as those found in learning-based methods. A model predictive control (MPC) framework is used to create a minimally invasive algorithm that certifies whether a desired control input is safe and can be applied to the vehicle, or that provides an alternate input to keep the vehicle in bounds. To this end, we provide a principled procedure to compute a safe and invariant set for nonlinear dynamic bicycle models using efficient convex approximation techniques. To fully support an aggressive racing performance without conservative safety interventions, the safe set is extended in real-time through predictive control backup trajectories. Applications for assisted manual driving and deep imitation learning on a miniature remote-controlled vehicle demonstrate the safety filter's ability to ensure vehicle safety during aggressive maneuvers.

中文翻译：

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基于学习的赛车控制的预测安全过滤器

在诸如自动驾驶之类的对安全至关重要的应用中，对高性能控制器的需求不断增长，这已经推动了形式安全验证技术的发展。在本文中，我们设计并实现了一种预测性安全过滤器，当与任何潜在的不安全控制信号（例如基于学习的方法中发现的信号）配对使用时，它能够在轨道边界方面保持车辆安全。模型预测控制（MPC）框架用于创建微创算法，以证明所需的控制输入是否安全并且可以应用于车辆，或者提供备用输入以使车辆保持在边界内。为此，我们提供了使用有效凸近似技术为非线性动态自行车模型计算安全不变集的原则程序。为了在没有保守的安全干预的情况下完全支持具有挑战性的赛车性能，可通过预测控制后备轨迹实时扩展安全装置。微型遥控车辆上的辅助手动驾驶和深度模仿学习应用程序证明了安全滤清器能够在激进机动期间确保车辆安全。