Bridging the Gap: Applying Assurance Arguments to MIL-HDBK-516C Certification of a Neural Network Control System with ASIF Run Time Assurance Architecture

no code implementations • 27 Mar 2023 • Jonathan Rowanhill, Ashlie B. Hocking, Aditya Zutshi, Kerianne L. Hobbs

This work examines leveraging the core theory behind ASIF as assurance argument explaining novel satisfaction of 516C compliance criteria.

Systems Theoretic Process Analysis of a Run Time Assured Neural Network Control System

no code implementations • 1 Sep 2022 • Kerianne L. Hobbs, Benjamin K. Heiner, Lillian Busse, Kyle Dunlap, Jonathan Rowanhill, Ashlie B. Hocking, Aditya Zutshi

This research considers the problem of identifying safety constraints and developing Run Time Assurance (RTA) for Deep Reinforcement Learning (RL) Tactical Autopilots that use neural network control systems (NNCS).

Collision Avoidance Reinforcement Learning (RL)

Statistical Verification of Autonomous Systems using Surrogate Models and Conformal Inference

no code implementations • 1 Apr 2020 • Chuchu Fan, Xin Qin, Yuan Xia, Aditya Zutshi, Jyotirmoy Deshmukh

Our technique uses model simulations to learn {\em surrogate models}, and uses {\em conformal inference} to provide probabilistic guarantees on the satisfaction of a given STL property.

Autonomous Vehicles Prediction Intervals