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.

翻译：控制和感知中的不确定性给自动驾驶车辆在非结构化环境中的导航带来了挑战，导致导航失败及车辆潜在损坏。本文提出了一种框架，通过最小化控制与感知不确定性来确保安全可靠的导航。该框架包含两种不确定性感知模型：基于学习的车辆动力学模型和自监督可通行性估计模型。我们训练了一种能够量化模型认知不确定性的车辆动力学模型，以实现主动探索，从而高效收集训练数据并有效规避不确定的状态-动作空间。此外，我们采用元学习训练可通行性代价预测网络。该模型可利用多种地形类型的行驶数据进行训练，并基于交互经验进行在线自适应，以降低偶然不确定性。将动力学模型和可通行性代价预测模型与基于采样的模型预测控制器相结合，能够优化轨迹以避免不确定地形和状态-动作空间。实验结果表明，所提出的方法降低了预测不确定性，并提升了自动驾驶车辆在非结构化环境中导航的稳定性。