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Abstract
Many autonomous systems, such as driverless taxis, perform safety-critical functions. Autonomous systems employ artificial intelligence (AI) techniques, specifically for environmental 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, that is, identifying data that differ in some respect from the data used for training, becomes a safety measure for system development and operation. In this study, 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 the literature by minimizing false negatives.
Highlights
Autonomous systems have achieved success in many application domains, including autonomous vehicles (AVs), smart home systems, and autonomous financial agents
We propose a new architecture for autoencoder-based semantic novelty detection with two innovations: (a) architectural guidelines for a semantic autoencoder topology that provokes the autoencoder to reconstruct novel input data with a distinct sufficient reconstruction error and (b) an architectural blueprint for a semantic error calculation as novelty criteria that calculates the difference between the original and the reconstructed input based on the resulting error of the corresponding output information
The remainder of this paper is structured as follows: In Section 2, we introduce a quick overview of the dependability cage monitoring architecture for autonomous systems; in Section 3, we provide an overview of related work, artificial intelligence (AI)-based novelty detection; in Section 4, the main research hypotheses are introduced; to evaluate the proposed concepts and research hypotheses, we describe, in Section 5, the evaluation framework, which we have used; in Sections 6–8, the architecture of the autoencoder-based semantic novelty detection is incrementally developed and evaluated concerning our three research hypotheses; in Section 9, we provide a brief summary of conclusions and future work
Summary
Autonomous systems have achieved success in many application domains, including autonomous vehicles (AVs), smart home systems, and autonomous financial agents. Autonomous systems are becoming more useful and beneficial for us. We, the users, increasingly rely on their services, even in safety-critical applications such as driverless taxis [1,2]. Many recent advancements in the performance of autonomous systems have been made possible by the application of machine learning (ML) techniques [3]. Autonomous systems are hybrid AI-based systems, integrating classical engineered subsystems combined with subsystems using artificial intelligence (AI) techniques. Vehicle controllers are classical engineered subsystems, whereas the perception subsystems of AVs are nowadays mainly AI based; both are integrated in an AV and together perform safety-critical functions
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