Recent advances in autonomous driving for uncrewed ground vehicles (UGVs) have spurred significant development, particularly in challenging terrains. This paper introduces a classification system assessing various UGV deployments reported in the literature. Our approach considers motion distortion features that include internal UGV features, such as mass and speed, and external features, such as terrain complexity, which all influence the efficiency of models and navigation systems. We present results that map UGV deployments relative to vehicle kinetic energy and terrain complexity, providing insights into the level of complexity and risk associated with different operational environments. Additionally, we propose a motion distortion metric to assess UGV navigation performance that does not require an explicit quantification of motion distortion features. Using this metric, we conduct a case study to illustrate the impact of motion distortion features on modeling accuracy. This research advocates for creating a comprehensive database containing many different motion distortion features, which would contribute to advancing the understanding of autonomous driving capabilities in rough conditions and provide a validation framework for future developments in UGV navigation systems.

翻译：近年来，无人地面车辆（UGV）在复杂地形中的自主驾驶技术取得显著进展。本文提出一种分类系统，用于评估文献中记载的各类UGV部署案例。该方法综合考虑运动畸变特征，包括UGV内部特征（如质量与速度）和外部特征（如地形复杂度），这些因素共同影响模型效率与导航系统性能。我们通过映射UGV部署与车辆动能及地形复杂度之间的关系，揭示不同运行环境所对应的复杂程度与风险等级。此外，我们提出一种无需显式量化运动畸变特征即可评估UGV导航性能的运动畸变指标。基于该指标，我们通过案例研究展示了运动畸变特征对建模精度的影响。本研究倡导构建包含多样化运动畸变特征的综合性数据库，这将有助于深化对复杂条件下自主驾驶能力的认知，并为未来UGV导航系统的发展提供验证框架。