  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.
