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.

在线安全验证自主系统的一项新技术，通过使用神经屏障证书有效地进行有界和无界时域的可达性分析，我们的方法使用由参数化神经网络给出的屏障证书，这些证书依赖于给定的初始集合，不安全集合和时间范围。