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

Off-road autonomy validation presents unique challenges due to the unpredictable and dynamic nature of off-road environments. Variability analyses, by sequentially sweeping across the parameter space, struggle to comprehensively assess the performance 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.