Modern AI techniques open up ever-increasing possibilities for autonomous vehicles, but how to appropriately verify the reliability of such systems remains unclear. A common approach is to conduct safety validation based on a predefined Operational Design Domain (ODD) describing specific conditions under which a system under test is required to operate properly. However, collecting sufficient realistic test cases to ensure comprehensive ODD coverage is challenging. In this paper, we report our practical experiences regarding the utility of data simulation with deep generative models for scenario-based ODD validation. We consider the specific use case of a camera-based rail-scene segmentation system designed to support autonomous train operation. We demonstrate the capabilities of semantically editing railway scenes with deep generative models to make a limited amount of test data more representative. We also show how our approach helps to analyze the degree to which a system complies with typical ODD requirements. Specifically, we focus on evaluating proper operation under different lighting and weather conditions as well as while transitioning between them.

翻译：现代人工智能技术为自动驾驶车辆带来了日益广阔的发展前景，但如何恰当验证此类系统的可靠性仍不明确。一种常见方法是基于预定义的运行设计域（ODD）进行安全验证，该域描述了被测系统需正常运行的特定条件。然而，收集足够真实的测试用例以全面覆盖ODD仍具挑战性。本文报告了我们运用深度生成模型进行数据仿真以开展场景化ODD验证的实践经验。我们以支持自主列车运行的摄像头铁路场景分割系统为具体用例，展示了利用深度生成模型对铁路场景进行语义编辑的能力，使有限测试数据更具代表性。同时，我们展示了该方法如何帮助分析系统对典型ODD要求的符合程度。具体而言，我们重点评估了在不同光照与天气条件及其过渡场景下系统的正常运行能力。