Software fault propagation patterns for model-based safety assessment in autonomous cars - LAAS - Laboratoire d'Analyse et d'Architecture des Systèmes
Communication Dans Un Congrès Année : 2022

Software fault propagation patterns for model-based safety assessment in autonomous cars

Résumé

The development of driver assistance and autonomous driving systems for vehicles has started to revolutionize the transportation sector, promising comfort, and safety. While significant technological progress has already been made in this area, many challenges remain. Among these challenges, ensuring safety has become even more critical due to the increasing use of complex, communicating, and reconfigurable embedded software. Current solutions to address safety include the use of model-based approaches for safety analyses instead of the traditional document-based safety analysis that is both informal and inefficient when faced with complexity. To this end, and in the context of automotive embedded software, we propose to rely on the use of fault patterns to improve the construction of software models used to conduct safety analyses. This paper makes a methodological proposal that improves current practices in terms of facilitated model construction and reusability, and that has been validated on the study of an automotive software component.
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Dates et versions

hal-03699226 , version 1 (20-06-2022)

Identifiants

  • HAL Id : hal-03699226 , version 1

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Yandika Sirgabsou, Claude Baron, Laurent Pahun, Philippe Esteban. Software fault propagation patterns for model-based safety assessment in autonomous cars. 11th European Congress on Embedded Real Time Systems (ERTS), Jun 2022, Toulouse, France. ⟨hal-03699226⟩
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