  Modeling dynamics is often the first step to making a vehicle autonomous.
While on-road autonomous vehicles have been extensively studied, off-road
vehicles pose many challenging modeling problems. An off-road vehicle
encounters highly complex and difficult-to-model terrain/vehicle interactions,
as well as having complex vehicle dynamics of its own. These complexities can
create challenges for effective high-speed control and planning. In this paper,
we introduce a framework for multistep dynamics prediction that explicitly
handles the accumulation of modeling error and remains scalable for
sampling-based controllers. Our method uses a specially-initialized Long
Short-Term Memory (LSTM) over a limited time horizon as the learned component
in a hybrid model to predict the dynamics of a 4-person seating all-terrain
vehicle (Polaris S4 1000 RZR) in two distinct environments. By only having the
LSTM predict over a fixed time horizon, we negate the need for long term
stability that is often a challenge when training recurrent neural networks.
Our framework is flexible as it only requires odometry information for labels.
Through extensive experimentation, we show that our method is able to predict
millions of possible trajectories in real-time, with a time horizon of five
seconds in challenging off road driving scenarios.
