高维的功能边界搜索用于自动驾驶系统的偏差强度测试
Yunwei Li1, Siyu Wu1, Anran Wang2
1School of Vehicle and Mobility, Tsinghua University, 100084, Beijing, China.
Accident; analysis and prevention
|August 15, 2025
概括
本研究引入了一个新的框架,用于生成自动驾驶系统 (ADS) 的安全边缘场景. 它通过补偿模型偏差来提高测试的准确性,提高了对系统功能边界的估计.
科学领域:
- 自主驾驶系统 自主驾驶系统
- 预期功能的安全性 (SOTIF)
- 系统工程 系统工程
背景情况:
- 测试和评估对于在自动驾驶系统 (ADS) 中验证预期功能的安全性 (SOTIF) 是至关重要的.
- 估计系统功能边界 (SFB) 需要为测试案例设计有效的安全边缘场景.
- 挑战包括维度的诅咒,测试覆盖范围的要求,以及黑子测试中的替代模型偏差.
研究的目的:
- 提出一个有效的框架,用于为ADS生成高维安全边缘场景.
- 开发一种方法来追踪测试中的系统 (SUT) 的系统功能边界 (SFB).
- 解决替代模型在场景生成和SFB估计中引入的不准确性.
主要方法:
- 使用基线代孕模型和多种群遗传算法 (MPGA) 来生成多样化的安全边际测试场景库.
- 使用系统功能边界跟踪 (SFBT) 模块来弥补代用模型和实际SUT之间的偏差.
- 适应性地概括关键场景以估计高维的功能边界.
主要成果:
- 拟议的框架有效地产生了高维安全边缘场景.
- 该SFBT模块有效地弥补模型偏差,导致更准确的SFB估计.
- 展示了关键场景的适应性概括的方法.
结论:
- 开发的框架提供了一种有效的方法,用于为ADS生成安全边缘场景.
- 它通过减轻替代模型偏差来提高系统功能边界 (SFB) 估计的准确性.
- 这个框架可以帮助测试和验证自动驾驶系统的操作设计领域 (ODD).
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