Quantitative Predictive Monitoring and Control for Safe Human-Machine Interaction

TL;DR

This paper introduces a novel predictive monitoring and control framework using Signal Temporal Logic with Uncertainty (STL-U) to enhance safety in human-machine interactions, demonstrated through healthcare and autonomous driving case studies.

Contribution

It develops a new STL-U based quantitative monitor, a loss function for Bayesian uncertainty calibration, and an adaptive control method for safer human-machine interactions.

Findings

Improves safety in Type 1 Diabetes management.

Enhances effectiveness in semi-autonomous driving.

Demonstrates robustness of the approach in case studies.

Abstract

There is a growing trend toward AI systems interacting with humans to revolutionize a range of application domains such as healthcare and transportation. However, unsafe human-machine interaction can lead to catastrophic failures. We propose a novel approach that predicts future states by accounting for the uncertainty of human interaction, monitors whether predictions satisfy or violate safety requirements, and adapts control actions based on the predictive monitoring results. Specifically, we develop a new quantitative predictive monitor based on Signal Temporal Logic with Uncertainty (STL-U) to compute a robustness degree interval, which indicates the extent to which a sequence of uncertain predictions satisfies or violates an STL-U requirement. We also develop a new loss function to guide the uncertainty calibration of Bayesian deep learning and a new adaptive control method, both of which leverage STL-U quantitative predictive monitoring results. We apply the proposed approach to two case studies: Type 1 Diabetes management and semi-autonomous driving. Experiments show that the proposed approach improves safety and effectiveness in both case studies.