Gaussian Process-based Interpretable Runtime Adaptation for Safe Autonomous Systems Operations in Unstructured Environments

Abstract

Autonomous vehicles may not behave as expected when subject to environmental disturbances. For instance, control commands suitable for driving on dry, paved roads may lead to unsafe conditions and undesired deviations when on slippery dirt or icy roads. Furthermore, it becomes increasingly important to offer human-understandable explanations of autonomous robots’ actions – especially when they operate in a space shared with humans such as public roads. In this work, we present an interpretable, risk-aware online adaptation approach that leverages a combination of Gaussian process regression and decision tree theory. Our approach predicts the system state in unknown environments using pre-trained models, which are continuously refined during runtime using a fast lookup table-based procedure. Every time the originally intended behavior is predicted to cause an unsafe state, our method computes a set of safe behaviors under the current model uncertainty and risk. The most suitable behavior is subsequently selected depending on mission requirements and a human-understandable explanation about the system’s decision-making is delivered. The proposed framework is applied to unmanned ground vehicles and validated in simulations, in which we demonstrate a successful safety-critical cargo delivery through a priori unknown, rough and slippery terrain.