FIDGET: Deep Learning-Based Fault Injection Framework for Safety Analysis and Intelligent Generation of Labeled Training Data

Fabarisov, Tagir; Morozov, Andrey; Mamaev, Ilshat; Grimmeisen, Philipp

doi:10.1109/etfa52439.2022.9921507

Cited by 2 publications

(1 citation statement)

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“…neural networks), 3) Sensor fusion function for world-modeling. These components can be associated with different fault parameters: 1) Failure-in-Time (FIT) and ageing factor Fabarisov et al (2022). 2) Gaussian noise and solid occlusions Jha et al (2018).…”

Section: Specifying Fault Injectionmentioning

confidence: 99%

Enhancing Safety Assurance for Automated Driving Systems by Supporting Operation Simulation and Data Analysis

Su,

Kang,

Tahmasebi

et al. 2023

Proceeding of the 33rd European Safety and Reliability Conference

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Automated Driving Systems (ADS) employ various techniques for operation perception, task planning and vehicle control. For driving on public roads, it is critical to guarantee the operational safety of such systems by attaining Minimal Risk Condition (MRC) despite unexpected environmental disruptions, human errors, functional faults and security attacks. This paper proposes a methodology to automatically identify potentially highly critical operational conditions by leveraging the design-time information in terms of vehicle architecture models and environment models. To identify the critical operating conditions, these design-time models are combined systematically with a variety of faults models for revealing the system behaviours in the presence of anomalies. The contributions of this paper are summarized as follows: 1) The design of a method for extracting related internal and external operational conditions from different system models. 2) The design of software services for identifying critical parameters and synthesizing operational data with fault injection. 3) The design for supporting operation simulation and data analysis.

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Section: Specifying Fault Injectionmentioning

confidence: 99%