Safe Reach Set Computation via Neural Barrier Certificates
This addresses safety verification for autonomous systems, offering an incremental improvement by generalizing barrier certificates to new state space regions.
The paper tackles the problem of online safety verification for autonomous systems by using neural barrier certificates to efficiently compute reachable sets for both bounded and unbounded horizons, demonstrating the approach on case studies including linear and nonlinear control-dependent models for autonomous driving scenarios.
We present a novel technique for online safety verification of autonomous systems, which performs reachability analysis efficiently for both bounded and unbounded horizons by employing neural barrier certificates. Our approach uses barrier certificates given by parameterized neural networks that depend on a given initial set, unsafe sets, and time horizon. Such networks are trained efficiently offline using system simulations sampled from regions of the state space. We then employ a meta-neural network to generalize the barrier certificates to state space regions that are outside the training set. These certificates are generated and validated online as sound over-approximations of the reachable states, thus either ensuring system safety or activating appropriate alternative actions in unsafe scenarios. We demonstrate our technique on case studies from linear models to nonlinear control-dependent models for online autonomous driving scenarios.