用AI生成多样路况，提升自动驾驶测试全面性
AI-Augmented Metamorphic Testing for Comprehensive Validation of Autonomous Vehicles
- 结合Stable Diffusion等AI工具生成多样化驾驶场景
- 可重复生成带天气、车道线等变化的测试场景
- 适合自动驾驶安全验证与测试团队使用
自动驾驶汽车有望彻底改变交通方式，但其安全性保障仍是重大挑战。这些系统需应对道路上各种意外情况，而其复杂性给全面测试带来巨大困难。传统测试方法存在‘预言家问题’（难以判断系统行为是否正确）及无法穷尽复现自动驾驶可能遇到的各种情境等问题。虽然元测试（Metamorphic Testing, MT）提供了部分解决方案，但其应用常受限于测试场景修改过于简单。本文提出通过集成AI图像生成工具（如Stable Diffusion）增强MT，以在运行设计域（ODD）内生成具有细微差异的驾驶场景，例如改变天气条件、调整环境元素或车道线布局，同时保留关键评估特征。该方法实现测试可重现、测试标准高效复用，并支持对自动驾驶系统在多样化场景下的表现进行综合评估，有效弥补现有测试实践的关键缺口。
原文摘要 · Abstract (English)
Self-driving cars have the potential to revolutionize transportation, but ensuring their safety remains a significant challenge. These systems must navigate a variety of unexpected scenarios on the road, and their complexity poses substantial difficulties for thorough testing. Conventional testing methodologies face critical limitations, including the oracle problem determining whether the systems behavior is correct and the inability to exhaustively recreate a range of situations a self-driving car may encounter. While Metamorphic Testing (MT) offers a partial solution to these challenges, its application is often limited by simplistic modifications to test scenarios. In this position paper, we propose enhancing MT by integrating AI-driven image generation tools, such as Stable Diffusion, to improve testing methodologies. These tools can generate nuanced variations of driving scenarios within the operational design domain (ODD)for example, altering weather conditions, modifying environmental elements, or adjusting lane markings while preserving the critical features necessary for system evaluation. This approach enables reproducible testing, efficient reuse of test criteria, and comprehensive evaluation of a self-driving systems performance across diverse scenarios, thereby addressing key gaps in current testing practices.
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