Many autonomous systems, such as driverless taxis, perform safety critical functions. Autonomous systems employ artificial intelligence (AI) techniques, specifically for the environment perception. Engineers cannot completely test or formally verify AI-based autonomous systems. The accuracy of AI-based systems depends on the quality of training data. Thus, novelty detection - identifying data that differ in some respect from the data used for training - becomes a safety measure for system development and operation. In this paper, we propose a new architecture for autoencoder-based semantic novelty detection with two innovations: architectural guidelines for a semantic autoencoder topology and a semantic error calculation as novelty criteria. We demonstrate that such a semantic novelty detection outperforms autoencoder-based novelty detection approaches known from literature by minimizing false negatives.
翻译:许多自主系统,例如没有司机的出租车,都发挥安全关键功能。自治系统采用人工智能技术,特别是环境认知技术。工程师无法完全测试或正式核查基于AI的自主系统。基于AI的系统的准确性取决于培训数据的质量。因此,新发现——查明在某些方面不同于培训数据的数据——成为系统开发和运行的安全措施。在本文件中,我们提出了基于自动编码的语义新颖检测新结构,有两种创新:语义自动读物表层学的建筑指南,以及作为新标准计算语义错误。我们证明,这种语义新颖的检测超越了从文献中知道的基于自动编码的新颖检测方法,最大限度地减少假的负值。