Off-Road Autonomy Validation Using Scalable Digital Twin Simulations Within High-Performance Computing Clusters

TL;DR

This paper introduces a scalable digital twin simulation framework using high-performance computing clusters to efficiently validate off-road autonomous systems across diverse conditions, addressing the limitations of traditional sequential testing methods.

Contribution

The paper presents a novel scalable simulation approach leveraging HPC clusters for off-road autonomy validation, enabling rapid, comprehensive testing and vulnerability analysis.

Findings

Effective parallelization of simulations on HPC clusters

Identification of potential vulnerabilities in autonomy modules

Demonstrated scalability and efficiency of the framework

Abstract

Off-road autonomy validation presents unique challenges due to the unpredictable and dynamic nature of off-road environments. Traditional methods focusing on sequentially sweeping across the parameter space for variability analysis struggle to comprehensively assess the performance and safety of off-road autonomous systems within the imposed time constraints. This paper proposes leveraging scalable digital twin simulations within high-performance computing (HPC) clusters to address this challenge. By harnessing the computational power of HPC clusters, our approach aims to provide a scalable and efficient means to validate off-road autonomy algorithms, enabling rapid iteration and testing of autonomy algorithms under various conditions. We demonstrate the effectiveness of our framework through performance evaluations of the HPC cluster in terms of simulation parallelization and present the systematic variability analysis of a candidate off-road autonomy algorithm to identify potential vulnerabilities in the autonomy stack's perception, planning and control modules.