  Current developments in autonomous off-road driving are steadily increasing
performance through higher speeds and more challenging, unstructured
environments. However, this operating regime subjects the vehicle to larger
inertial effects, where consideration of higher-order states is necessary to
avoid failures such as rollovers or excessive impact forces. Aggressive driving
through Model Predictive Control (MPC) in these conditions requires dynamics
models that accurately predict safety-critical information. This work aims to
empirically quantify this aggressive operating regime and its effects on the
performance of current models. We evaluate three dynamics models of varying
complexity on two distinct off-road driving datasets: one simulated and the
other real-world. By conditioning trajectory data on higher-order states, we
show that model accuracy degrades with aggressiveness and simpler models
degrade faster. These models are also validated across datasets, where
accuracies over safety-critical states are reported and provide benchmarks for
future work.
