Safe Recognition A.I. of a Railway Signal by On-Board Camera

Boulineau, Jean François

doi:10.1007/978-3-030-58462-7_1

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…gaps from pre-existing research on the safety assurance of AI [1], (ii.) merging sparse efforts and practice towards the safety assurance of AI into a systematic, process-oriented approach [23,30,38,[49][50][51][52][53][54][55][56][57], and (iii.) the expertise of the Safety ArtISt research team on safety and AI.…”

Section: Description Of the Safety Artist Methodsmentioning

confidence: 99%

Design and Assurance of Safety-Critical Systems with Artificial Intelligence in FPGAs: The Safety ArtISt Method and a Case Study of an FPGA-Based Autonomous Vehicle Braking Control System

Silva Neto,

Silva,

Camargo

et al. 2023

Electronics

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With the advancements in utilizing Artificial Intelligence (AI) in embedded safety-critical systems based on Field-Programmable Gate Arrays (FPGAs), assuring that these systems meet their safety requirements is of paramount importance for their revenue service. Based on this context, this paper has two main objectives. The first of them is to present the Safety ArtISt method, developed by the authors to guide the lifecycle of AI-based safety-critical systems, and emphasize its FPGA-oriented tasks and recommended practice towards safety assurance. The second one is to illustrate the application of Safety ArtISt with an FPGA-based braking control system for autonomous vehicles relying on explainable AI generated with High-Level Synthesis. The results indicate that Safety ArtISt played four main roles in the safety lifecycle of AI-based systems for FPGAs. Firstly, it provided guidance in identifying the safety-critical role of activities such as sensitivity analyses for numeric representation and FPGA dimensioning to achieve safety. Furthermore, it allowed building qualitative and quantitative safety arguments from analyses and physical experimentation with actual FPGAs. It also allowed the early detection of safety issues—thus reducing project costs—and, ultimately, it uncovered relevant challenges not discussed in detail when designing safety-critical, explainable AI for FPGAs.

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