A Systematic Digital Engineering Approach to Verification&Validation of Autonomous Ground Vehicles in Off-Road Environments

2025-03-18

AI Summary

Validating autonomous algorithms for unmanned ground vehicles (UGVs) in off-road environments faces challenges including complex test parameter spaces, difficulty in verification, and cumbersome variant management. Method: This paper proposes a tightly coupled digital engineering framework integrating Model-Based Systems Engineering (MBSE), Model-Based Design (MBD), and digital twin technologies. Requirements are modeled and traced using SysML; algorithm design and simulation are conducted in MATLAB/Simulink; and a high-fidelity digital twin enables multimodal, unstructured scenario modeling—spanning day/night cycles and diverse weather conditions—while supporting automated parametric test case generation, end-to-end automated execution, and a closed-loop digital thread (requirements → design → test → results). Contribution/Results: The framework is validated on a vision-servoed off-road mission for a light tactical vehicle, achieving fully automated testing across 128 scenario classes, automatic acquisition and analysis of key performance indicators, and fully traceable test reports and data. This work represents the first deep integration of MBSE, MBD, and digital twin for off-road autonomy, significantly enhancing development efficiency and verification credibility.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

The engineering community currently encounters significant challenges in the systematic development and validation of autonomy algorithms for off-road ground vehicles. These challenges are posed by unusually high test parameters and algorithmic variants. In order to address these pain points, this work presents an optimized digital engineering framework that tightly couples digital twin simulations with model-based systems engineering (MBSE) and model-based design (MBD) workflows. The efficacy of the proposed framework is demonstrated through an end-to-end case study of an autonomous light tactical vehicle (LTV) performing visual servoing to drive along a dirt road and reacting to any obstacles or environmental changes. The presented methodology allows for traceable requirements engineering, efficient variant management, granular parameter sweep setup, systematic test-case definition, and automated execution of the simulations. The candidate off-road autonomy algorithm is evaluated for satisfying requirements against a battery of 128 test cases, which is procedurally generated based on the test parameters (times of the day and weather conditions) and algorithmic variants (perception, planning, and control sub-systems). Finally, the test results and key performance indicators are logged, and the test report is generated automatically. This then allows for manual as well as automated data analysis with traceability and tractability across the digital thread.

Problem

• Systematic development of autonomy algorithms for off-road vehicles.
• High test parameters and algorithmic variants challenge validation.
• Digital twin simulations integrated with MBSE and MBD workflows.

Innovation

• Digital twin simulations coupled with MBSE and MBD workflows.
• Automated execution of simulations with traceable requirements engineering.
• Procedural generation of test cases for off-road autonomy algorithms.