运行时未知不安全场景的识别自动驾驶汽车的SOTIF
Cheng Wang1, Kai Storms2, Ning Zhang3
1School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, United Kingdom.
Accident; analysis and prevention
|December 6, 2023
概括
静音测试通过在后台运行测试来验证自动驾驶汽车 (AV) 感知系统,如车道标记检测算法. 这种方法有效地识别了拐角事件,并增强了AV安全验证,而不会破坏驾驶操作.
科学领域:
- 自主系统 自主系统
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
背景情况:
- 自动驾驶汽车 (AV) 的安全至关重要,但当前的测试方法在有效性,安全性和速度方面存在困难.
- 无声测试是一种背景验证技术,为这些挑战提供了一个有希望的解决方案.
研究的目的:
- 扩展静音测试用于AV感知系统验证,使用车道标记检测算法 (LMDA) 作为案例研究.
- 进行实地实验,以评估静音测试在现实世界AV系统验证中的有效性.
主要方法:
- 开发了一个架构来描述AV感知静音测试工作原理.
- 利用对抗性图像来评估LMDA在具有挑战性的条件下的表现.
- 创建了关键的行人交叉场景,以测试AV系统的响应.
主要成果:
- 通过使用定义的评估指标,成功识别了LMDA的角落案例.
- 证明了多个虚拟实例在扩大测试空间和发现未知的不安全场景方面的有效性.
- 突出了该方法在检测假阳性物体检测方面的能力.
结论:
- 静音测试为AV感知和系统验证提供了有效,安全和高效的方法.
- 这种方法有助于大大提高自动驾驶汽车的整体安全性和可靠性.
- 采用多样化的虚拟实例可以提高边缘案例和潜在安全漏洞的发现.
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