ShieldNN: A Provably Safe NN Filter for Unsafe NN Controllers

In this paper, we consider the problem of creating a safe-by-design Rectified Linear Unit (ReLU) Neural Network (NN), which, when composed with an arbitrary control NN, makes the composition provably safe. In particular, we propose an algorithm to synthesize such NN filters that safely correct control inputs generated for the continuous-time Kinematic Bicycle Model (KBM)... (read more)

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