16,666 research outputs found
Neural Networks for Safety-Critical Applications - Challenges, Experiments and Perspectives
We propose a methodology for designing dependable Artificial Neural Networks
(ANN) by extending the concepts of understandability, correctness, and validity
that are crucial ingredients in existing certification standards. We apply the
concept in a concrete case study in designing a high-way ANN-based motion
predictor to guarantee safety properties such as impossibility for the ego
vehicle to suggest moving to the right lane if there exists another vehicle on
its right.Comment: Summary for activities conducted in the fortiss
Eigenforschungsprojekt "TdpSW - Towards dependable and predictable SW for
ML-based autonomous systems". All ANN-based motion predictors being formally
analyzed are available in the source fil
Automated Synthesis of SEU Tolerant Architectures from OO Descriptions
SEU faults are a well-known problem in aerospace environment but recently their relevance grew up also at ground level in commodity applications coupled, in this frame, with strong economic constraints in terms of costs reduction. On the other hand, latest hardware description languages and synthesis tools allow reducing the boundary between software and hardware domains making the high-level descriptions of hardware components very similar to software programs. Moving from these considerations, the present paper analyses the possibility of reusing Software Implemented Hardware Fault Tolerance (SIHFT) techniques, typically exploited in micro-processor based systems, to design SEU tolerant architectures. The main characteristics of SIHFT techniques have been examined as well as how they have to be modified to be compatible with the synthesis flow. A complete environment is provided to automate the design instrumentation using the proposed techniques, and to perform fault injection experiments both at behavioural and gate level. Preliminary results presented in this paper show the effectiveness of the approach in terms of reliability improvement and reduced design effort
Validation of a software dependability tool via fault injection experiments
Presents the validation of the strategies employed in the RECCO tool to analyze a C/C++ software; the RECCO compiler scans C/C++ source code to extract information about the significance of the variables that populate the program and the code structure itself. Experimental results gathered on an Open Source Router are used to compare and correlate two sets of critical variables, one obtained by fault injection experiments, and the other applying the RECCO tool, respectively. Then the two sets are analyzed, compared, and correlated to prove the effectiveness of RECCO's methodology
Validation of Ultrahigh Dependability for Software-Based Systems
Modern society depends on computers for a number of critical tasks in which failure can have very high costs. As a consequence, high levels of dependability (reliability, safety, etc.) are required from such computers, including their software. Whenever a quantitative approach to risk is adopted, these requirements must be stated in quantitative terms, and a rigorous demonstration of their being attained is necessary. For software used in the most critical roles, such demonstrations are not usually supplied. The fact is that the dependability requirements often lie near the limit of the current state of the art, or beyond, in terms not only of the ability to satisfy them, but also, and more often, of the ability to demonstrate that they are satisfied in the individual operational products (validation). We discuss reasons why such demonstrations cannot usually be provided with the means available: reliability growth models, testing with stable reliability, structural dependability modelling, as well as more informal arguments based on good engineering practice. We state some rigorous arguments about the limits of what can be validated with each of such means. Combining evidence from these different sources would seem to raise the levels that can be validated; yet this improvement is not such as to solve the problem. It appears that engineering practice must take into account the fact that no solution exists, at present, for the validation of ultra-high dependability in systems relying on complex software
BlockChain: A distributed solution to automotive security and privacy
Interconnected smart vehicles offer a range of sophisticated services that
benefit the vehicle owners, transport authorities, car manufacturers and other
service providers. This potentially exposes smart vehicles to a range of
security and privacy threats such as location tracking or remote hijacking of
the vehicle. In this article, we argue that BlockChain (BC), a disruptive
technology that has found many applications from cryptocurrencies to smart
contracts, is a potential solution to these challenges. We propose a BC-based
architecture to protect the privacy of the users and to increase the security
of the vehicular ecosystem. Wireless remote software updates and other emerging
services such as dynamic vehicle insurance fees, are used to illustrate the
efficacy of the proposed security architecture. We also qualitatively argue the
resilience of the architecture against common security attacks
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