Safe Navigation in Unstructured Environments by Minimizing Uncertainty in Control and Perception

• URL: http://arxiv.org/abs/2306.14601v1
• Date: Mon, 26 Jun 2023 11:24:03 GMT
• Title: Safe Navigation in Unstructured Environments by Minimizing Uncertainty in Control and Perception
• Authors: Junwon Seo, Jungwi Mun, and Taekyung Kim
• Abstract summary: Uncertainty in control and perception poses challenges for autonomous vehicle navigation in unstructured environments. This paper introduces a framework that minimizes control and perception uncertainty to ensure safe and reliable navigation.
• Score: 5.46262127926284
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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