Autonomous unmanned ground vehicles (UGVs) are beginning to play a more critical role in military operations. As the size of the fighting forces continues to draw down, the U.S. and coalition partner Armed Forces will become increasingly reliant on UGVs to perform mission-critical roles. These roles range from squad-level manned-unmanned teaming to large-scale autonomous convoy operations. However, as more UGVs with increasing levels of autonomy are entering the field, tools for accurately predicting these UGVs performance and capabilities are lacking. In particular, the mobility of autonomous UGVs is a largely unsolved problem. While legacy tools for predicting ground vehicle mobility are available for both assessing performance and planning operations, in particular the NATO Reference Mobility Model, no such toolset exists for autonomous UGVs. Once autonomy comes into play, ground vehicle mechanical-mobility is no longer enough to characterize vehicle mobility performance. Not only will vehicle-terrain interactions and driver concerns impact mobility, but sensor-environment interactions will also affect mobility. UGV mobility will depend in a large part on the sensor data available to drive the UGVs autonomy algorithms. A limited amount of research has been focused on the concept of perception-based mobility to date. To that end, the presented work will provide a review of the tools and methods developed thus far for modeling, simulating, and assessing autonomous mobility for UGVs. This review will highlight both the modifications being made to current mobility modeling tools and new tools in development specifically for autonomous mobility modeling. In light of this review, areas of current need will also be highlighted, and recommended steps forward will be proposed.