用可微逻辑训练安全导航神经网络，确保自动驾驶行为可靠。
Creating a Formally Verified Neural Network for Autonomous Navigation: An Experience Report
- 结合可微逻辑设计神经网络，从架构上保证基础安全属性。
- 在自定义数据集上训练模型，实现视觉导航任务。
- 验证工具实测表明能有效检测潜在安全缺陷，适合高可靠性系统。
自动驾驶车辆对神经网络的依赖日益增加，带来了验证挑战。本文报告了一项案例研究，探索在自定义数据集上设计与训练用于视觉自主导航的神经网络。我们特别关注使用可微逻辑的方法，使神经网络在设计阶段即满足基本安全属性，从而保证训练后的行为符合预期。文中论证了适用于本研究的神经网络验证工具的选择依据，并报告了在自动驾驶系统中使用神经网络验证工具的观察结果。
原文摘要 · Abstract (English)
The increased reliance of self-driving vehicles on neural networks opens up the challenge of their verification. In this paper we present an experience report, describing a case study which we undertook to explore the design and training of a neural network on a custom dataset for vision-based autonomous navigation. We are particularly interested in the use of machine learning with differentiable logics to obtain networks satisfying basic safety properties by design, guaranteeing the behaviour of the neural network after training. We motivate the choice of a suitable neural network verifier for our purposes and report our observations on the use of neural network verifiers for self-driving systems.
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