Araújo Bernardo, Teixeira João F, Fonseca Joaquim, Cerqueira Ricardo, Beco Sofia C
Bosch Car Multimedia S.A., 4705-820 Braga, Portugal.
Entropy (Basel). 2024 Jul 26;26(8):634. doi: 10.3390/e26080634.
Deep learning approaches have been gaining importance in several applications. However, the widespread use of these methods in safety-critical domains, such as Autonomous Driving, is still dependent on their reliability and trustworthiness. The goal of this paper is to provide a review of deep learning-based uncertainty methods and their applications to support perception tasks for Autonomous Driving. We detail significant Uncertainty Quantification and calibration methods, and their contributions and limitations, as well as important metrics and concepts. We present an overview of the state of the art of out-of-distribution detection and active learning, where uncertainty estimates are commonly applied. We show how these methods have been applied in the automotive context, providing a comprehensive analysis of reliable AI for Autonomous Driving. Finally, challenges and opportunities for future work are discussed for each topic.
深度学习方法在多个应用领域中日益重要。然而，这些方法在诸如自动驾驶等对安全至关重要的领域中的广泛应用仍取决于其可靠性和可信度。本文的目标是对基于深度学习的不确定性方法及其在支持自动驾驶感知任务中的应用进行综述。我们详细介绍了重要的不确定性量化和校准方法、它们的贡献和局限性，以及重要的指标和概念。我们概述了分布外检测和主动学习的技术现状，不确定性估计在其中得到了广泛应用。我们展示了这些方法在汽车领域中的应用方式，对自动驾驶可靠人工智能进行了全面分析。最后，针对每个主题讨论了未来工作的挑战和机遇。
