Safe Navigation in Unstructured Environments by Minimizing Uncertainty
  in Control and Perception
Junwon Seo Jungwi Mun Taekyung Kim
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.