Istituto di Scienza e Tecnologie dell'Informazione     
Falcini F., Lami G. Deep learning in automotive: challenges and opportunities. In: SPICE 2017 - Software Process Improvement and Capability Determination. 17th International Conference (Palma de Mallorca, Spain, 4-5 October 2017). Proceedings, pp. 279 - 288. Antonia Mas, Antoni Mesquida, Rory V. O'Connor, Terry Rout, Alec Dorling (eds.). (Communications in Computer and Information Science, vol. 770). Springer, 2017.
The interest of the automotive industry in deep-learning-based technology is growing and related applications are going to be pervasively used in the modern automobiles. Automotive is a domain where different standards addressing the software development process apply, as Automotive SPICE and, for functional safety relevant products, ISO 26262. So, in the automotive software engineering community, the awareness of the need to integrate deep- learning-based development with development approaches derived from these standards is growing, at the technical, methodological, and cultural levels. This paper starts from a lifecycle for deep-learning-based development defined by the authors, called W-model, and addresses the issue of the applicability of Automotive SPICE to deep-learning-based developments. A conceptual mapping between Automotive SPICE and the deep learning lifecycles phases is provided in this paper with the aim of highlighting the open issues related to the applicability of automotive software development standards to deep learning.
URL: http://https://link.springer.com/chapter/10.1007/978-3-319-67383-7_21
DOI: 10.1007/978-3-319-67383-7_21
Subject Deep Learning
W model
Software development process management
I. Computing methodologies

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