Challenges in Assessing Safety Critical Autonomous Systems

2017 ◽  
Vol 2017 (0) ◽  
pp. J1810204
Author(s):  
Joachim IDEN
2021 ◽  
Vol 34 (5) ◽  
pp. 1723-1742
Author(s):  
Wei Xiao ◽  
G. Christos Cassandras ◽  
Calin Belta

2016 ◽  
Vol 232 ◽  
pp. 79-90 ◽  
Author(s):  
Adina Aniculaesei ◽  
Daniel Arnsberger ◽  
Falk Howar ◽  
Andreas Rausch

Author(s):  
Mo Chen ◽  
Claire J. Tomlin

Autonomous systems are becoming pervasive in everyday life, and many of these systems are complex and safety-critical. Formal verification is important for providing performance and safety guarantees for these systems. In particular, Hamilton–Jacobi (HJ) reachability is a formal verification tool for nonlinear and hybrid systems; however, it is computationally intractable for analyzing complex systems, and computational burden is in general a difficult challenge in formal verification. In this review, we begin by briefly presenting background on reachability analysis with an emphasis on the HJ formulation. We then present recent work showing how high-dimensional reachability verification can be made more tractable by focusing on two areas of development: system decomposition for general nonlinear systems, and traffic protocols for unmanned airspace management. By tackling the curse of dimensionality, tractable verification of practical systems is becoming a reality, paving the way for more pervasive and safer automation.


2021 ◽  
Vol 11 (21) ◽  
pp. 9881
Author(s):  
Andreas Rausch ◽  
Azarmidokht Motamedi Sedeh ◽  
Meng Zhang

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.


Author(s):  
Zhibin Yang ◽  
Yang Bao ◽  
Yongqiang Yang ◽  
Zhiqiu Huang ◽  
Jean-Paul Bodeveix ◽  
...  

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