HPCクラスタ上のスケーラブルなデジタルツインシミュレーションによるオフロード自律走行の検証
Off-Road Autonomy Validation Using Scalable Digital Twin Simulations Within High-Performance Computing Clusters
高性能計算クラスタ上でスケーラブルなデジタルツインシミュレーションを活用し、オフロード自律走行アルゴリズムの検証を効率化するフレームワークを提案し、その有効性を評価した。
著者: Tanmay Vilas Samak, Chinmay Vilas Samak, Joey Binz, Jonathon Smereka, Mark Brudnak, David Gorsich, Feng Luo, Venkat Krovi
分類: cs.RO, cs.DC
原文アブストラクト
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.
