A predictive safety filter for learning-based racing control

Abstract

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.

Onboard Safety Guarantees for Racing Drones: High-Speed Geofencing With Control Barrier Functions

“…In [10], the authors propose a sampling-based MPC approach that generates many possible safe trajectories at each time-step, and chooses the one closest to the user's desired input subject to safety conditions. Another MPC-based approach is demonstrated in [11], which uses learning to minimize conservatism, but neither of these approaches are able to run in real-time on a microcontroller. In the context of geofencing, [12] presents an MPC-based approach, but it lacks the guaranteed feasibility of [10].…”