Safety Verification of Neural Network Control Systems Using Guaranteed Neural Network Model Reduction
This work addresses computational bottlenecks in safety verification for neural network control systems, though it appears incremental as it builds on existing reachability-based methods.
The paper tackles the computational inefficiency of safety verification for neural network control systems by developing a guaranteed neural network model reduction method, which significantly reduces verification time in an Adaptive Cruise Control case study.
This paper aims to enhance the computational efficiency of safety verification of neural network control systems by developing a guaranteed neural network model reduction method. First, a concept of model reduction precision is proposed to describe the guaranteed distance between the outputs of a neural network and its reduced-size version. A reachability-based algorithm is proposed to accurately compute the model reduction precision. Then, by substituting a reduced-size neural network controller into the closed-loop system, an algorithm to compute the reachable set of the original system is developed, which is able to support much more computationally efficient safety verification processes. Finally, the developed methods are applied to a case study of the Adaptive Cruise Control system with a neural network controller, which is shown to significantly reduce the computational time of safety verification and thus validate the effectiveness of the method.