  In this paper, we investigate a hybrid scheme that combines nonlinear model
predictive control (MPC) and model-based reinforcement learning (RL) for
navigation planning of an autonomous model car across offroad, unstructured
terrains without relying on predefined maps. Our innovative approach takes
inspiration from BADGR, an LSTM-based network that primarily concentrates on
environment modeling, but distinguishes itself by substituting LSTM modules
with transformers to greatly elevate the performance our model. Addressing
uncertainty within the system, we train an ensemble of predictive models and
estimate the mutual information between model weights and outputs, facilitating
dynamic horizon planning through the introduction of variable speeds. Further
enhancing our methodology, we incorporate a nonlinear MPC controller that
accounts for the intricacies of the vehicle's model and states. The model-based
RL facet produces steering angles and quantifies inherent uncertainty. At the
same time, the nonlinear MPC suggests optimal throttle settings, striking a
balance between goal attainment speed and managing model uncertainty influenced
by velocity. In the conducted studies, our approach excels over the existing
baseline by consistently achieving higher metric values in predicting future
events and seamlessly integrating the vehicle's kinematic model for enhanced
decision-making. The code and the evaluation data are available at
https://github.com/FARAZLOTFI/offroad_autonomous_navigation/).
