  Uncertainty in control and perception poses challenges for autonomous vehicle
navigation in unstructured environments, leading to navigation failures and
potential vehicle damage. This paper introduces a framework that minimizes
control and perception uncertainty to ensure safe and reliable navigation. The
framework consists of two uncertainty-aware models: a learning-based vehicle
dynamics model and a self-supervised traversability estimation model. We train
a vehicle dynamics model that can quantify the epistemic uncertainty of the
model to perform active exploration, resulting in the efficient collection of
training data and effective avoidance of uncertain state-action spaces. In
addition, we employ meta-learning to train a traversability cost prediction
network. The model can be trained with driving data from a variety of types of
terrain, and it can online-adapt based on interaction experiences to reduce the
aleatoric uncertainty. Integrating the dynamics model and traversability cost
prediction model with a sampling-based model predictive controller allows for
optimizing trajectories that avoid uncertain terrains and state-action spaces.
Experimental results demonstrate that the proposed method reduces uncertainty
in prediction and improves stability in autonomous vehicle navigation in
unstructured environments.
