Predictive Safety Filter

Predictive safety filters are computational mechanisms designed to enhance the safety of autonomous systems, particularly those employing reinforcement learning or model predictive control, by ensuring adherence to safety constraints. Current research focuses on improving filter efficiency through techniques like system decomposition, gain scheduling, and multi-step prediction horizons, often incorporating model predictive control (MPC) or reinforcement learning (RL) architectures to achieve robust safety guarantees even under uncertainty. These filters are crucial for deploying autonomous systems in safety-critical applications like robotics, autonomous vehicles, and maritime navigation, mitigating risks associated with unpredictable environments and potential control failures.