State Prediction of Human-in-the-Loop Multi-rotor System with Stochastic Human Behavior Model

TL;DR

This paper introduces a stochastic human behavior model-based state prediction method for multi-rotor systems in near-collision scenarios, improving safety analysis by reducing conservativeness through probabilistic predictions.

Contribution

It presents a novel state prediction approach that incorporates a Gaussian Mixture Model of human behavior, enhancing reachability analysis accuracy in human-in-the-loop systems.

Findings

The method accurately predicts multi-rotor states considering human evasive maneuvers.

Experimental results validate the robustness of the probabilistic state predictions.

The approach reduces over-conservativeness in safety analysis of HiLCPS.

Abstract

Reachability analysis is a widely used method to analyze the safety of a Human-in-the-Loop Cyber Physical System (HiLCPS). This strategy allows the HiLCPS to respond against an imminent threat in advance by predicting reachable states of the system. However, it could lead to an unnecessarily conservative reachable set if the prediction only relies on the system dynamics without explicitly considering human behavior, and thus the risk might be overestimated. To reduce the conservativeness of the reachability analysis, we present a state prediction method which takes into account a stochastic human behavior model represented as a Gaussian Mixture Model (GMM). In this paper, we focus on the multi-rotor in a near-collision situation. The stochastic human behavior model is trained using experimental data to represent human operators' evasive maneuver. Then, we can retrieve a human control input probability distribution from the trained stochastic human behavior model using the Gaussian Mixture Regression (GMR). The proposed algorithm predicts the probability distribution of the multi-rotor's future state based on the given dynamics and the retrieved human control input probability distribution. Besides, the proposed state prediction method considers the uncertainty of the initial state modeled as a GMM, which yields more robust performance. Human subject experiment results are provided to demonstrate the effectiveness of the proposed algorithm.