This paper focuses on a Vision-based Landing task and presents the design and
the validation of a dataset that would comply with the Operational Design
Domain (ODD) of a Machine-Learning (ML) system. Relying on emerging
certification standards, we describe the process for establishing ODDs at both
the system and image levels. In the process, we present the translation of
high-level system constraints into actionable image-level properties, allowing
for the definition of verifiable Data Quality Requirements (DQRs). To
illustrate this approach, we use the Landing Approach Runway Detection (LARD)
dataset which combines synthetic imagery and real footage, and we focus on the
steps required to verify the DQRs. The replicable framework presented in this
paper addresses the challenges of designing a dataset compliant with the
stringent needs of ML-based systems certification in safety-critical
applications.

本研究聚焦于基于视觉的着陆任务，展示了符合机器学习系统操作设计领域（ODD）的数据集的设计和验证。通过借鉴新兴的认证标准，我们描述了在系统和图像级别建立 ODDs 的流程。在该过程中，我们将高级系统约束转化为可操作的图像级属性，从而定义了可验证的数据质量要求 (DQRs)。为了阐述这种方法，我们使用了 Landing Approach Runway Detection (LARD) 数据集，结合了合成图像和真实画面，并侧重于验证 DQRs 所需的步骤。本论文中提出的可复制框架解决了为安全关键应用中基于 ML 的系统认证设计符合严格需求的挑战。

如何设计符合基于机器学习系统的 ODD 的数据集

How to design a dataset compliant with an ML-based system ODD?

We propose a methodology for designing dependable Artificial Neural Networks
(ANN) by extending the concepts of understandability, correctness, and validity
that are crucial ingredients in existing certification standards. We apply the
concept in a concrete case study in designing a high-way ANN-based motion
predictor to guarantee safety properties such as impossibility for the ego
vehicle to suggest moving to the right lane if there exists another vehicle on
its right.

通过扩展现有认证标准中必不可少的可理解性、正确性和有效性等概念，我们提出了一种设计可靠人工神经网络（ANN）的方法论，并将其应用于一个具体的案例研究中，设计了一个高速公路 ANN 基础运动预测器，以确保安全特性，例如如果其右侧有其他车辆，则无法建议自车向右车道移动。