Evaluating Perception Systems for Autonomous Vehicles Using Quality Temporal Logic
Evaluating Perception Systems for Autonomous Vehicles Using Quality Temporal Logic
复制标题
使用质量时态逻辑评估自动驾驶汽车的感知系统
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Georgios Fainekos
中科院分区：
文献类型：
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作者：
Adel Dokhanchi;H. B. Amor;Jyotirmoy V. Deshmukh;Georgios Fainekos
为了在自动驾驶汽车应用中实现可靠的态势感知，我们需要开发强大可靠的图像处理和机器学习算法。目前，还没有一个通用的框架来推理感知系统的性能。本文介绍了时间质量时序逻辑（TQTL）作为一种形式语言，用于监测和测试自动驾驶汽车应用程序的目标检测和态势感知算法的性能。我们证明，它是可能的，以描述有趣的属性TQTL公式和检测的情况下，违反了属性。
For reliable situation awareness in autonomous vehicle applications, we need to develop robust and reliable image processing and machine learning algorithms. Currently, there is no general framework for reasoning about the performance of perception systems. This paper introduces Timed Quality Temporal Logic (TQTL) as a formal language for monitoring and testing the performance of object detection and situation awareness algorithms for autonomous vehicle applications. We demonstrate that it is possible to describe interesting properties as TQTL formulas and detect cases where the properties are violated.
影响因子：
9.2
作者：
Geiger, A.;Lenz, P.;Urtasun, R.
通讯作者：
Urtasun, R.